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This R markdown document (.Rmd) contains analysis, visualisation and statistical testing relating to research paper “Reticular adhesions mediate cell-matrix attachment during mitosis” by Lock et al (NCB, 2018).

This code uses the R markdown framework and enables production of statistical and graphical outputs equivalent to those presented in the paper. Some purely aesthetic differences exist to final paper figures given subsequent editing (adobe illustrator) for figure embedding. All R packages necessary to run this code will be installed automatically upon running this code.

This code calls the source data file named “Supplementary Table 1 Statistics Source Data.xlsx” and selects individual data sheets pertinent to each analysis. These sheets are named according the Figure Panel that they underpin. The user must place this source data file in a location of their choice and modify the “data_input_path” variable to reflect this location. Graphical outputs are saved according to the relevant Figure Panel names (with some additional descriptive information) to a location defined by the variable “panel_output_path”. By default this is in a folder named “Graphical_Outputs” within the defined “data_input_path”. Note that some figure panels were generated directly within Excel and these can be found within the source data file.

In addition to specific .svg graphical outputs, this R markdown code will produce an HTML notebook that is useful to collectively preview and efficiently share outputs and their underlying code. This output HTML notebook file matches the name of this source .Rmd file, i.e. “Statistical_Analysis_Code_Lock_et_al_NCB_2018.nb.html”.

**

Setup

Input Path

rr # Set your path to input data data_input_path <- /Users/johnlock/Dropbox/A-CMAC Manchester collaboration/NCB submission documents/Resubmission 2/Source_Data
# name of the source data file which you have placed in the above location data_file <- Table 1 Statistics Source Data.xlsx

Output Path

rr # Define panel output path panel_output_path <- paste(data_input_path, _Outputs/, sep = /)

Figure 1

Import Data for Figure 1

rr # Import individual data files Mass.Spec.Data <- read_xlsx(paste(data_input_path, data_file, sep = /), sheet = 1A, col_names = TRUE) b5.int.vs.VN.conc <- read_xlsx(paste(data_input_path, data_file, sep = /), sheet = 1G, col_names = TRUE)

Mass Spec of Integrin subunits in long-term culture

Figure 1A boxplot of Integrin subunits spectral counts

rr #define panel_file_name Integrin_Spectral_Counts_File_Name <- 1A Integrin Spectral Counts Boxplot
boxplot(Mean.Spectral.Counts ~ Integrin.Subunit, data = Mass.Spec.Data, notch = FALSE, varwidth = FALSE, las=1, ylab = Spectral Counts, col = , ylim = c(0, 50), outline = FALSE, boxlty = 0, whisklty = 0, staplelty = 0, medlwd = 0.5, boxfill = NA) beeswarm(Mean.Spectral.Counts ~ Integrin.Subunit, data = Mass.Spec.Data, method = ‘center’, add = T, col = , pch = 19, cex = 0.7) r svglite(paste(panel_output_path, Integrin_Spectral_Counts_File_Name, .svg, sep = \), width=8, height=6) boxplot(Mean.Spectral.Counts ~ Integrin.Subunit, data = Mass.Spec.Data, notch = FALSE, varwidth = FALSE, las=1, ylab = Spectral Counts, col = , ylim = c(0, 50), outline = FALSE, boxlty = 0, whisklty = 0, staplelty = 0, medlwd = 0.5, boxfill = NA) r beeswarm(Mean.Spectral.Counts ~ Integrin.Subunit, data = Mass.Spec.Data, method = ‘center’, add = T, col = , pch = 19, cex = 0.7) dev.off()

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T-testing for spectral count differences differences

rr AVvsB1 <- subset(Mass.Spec.Data, Integrin.Subunit ==  | Integrin.Subunit == 1) T_test_AVvsB1 <- t.test(Mean.Spectral.Counts ~ Integrin.Subunit, data = AVvsB1, alternative = .sided, paired = FALSE)$p.value T_test_AVvsB1

[1] 0.005444416

rr B5vsB1 <- subset(Mass.Spec.Data, Integrin.Subunit == 5 | Integrin.Subunit == 1) T_test_B5vsB1 <- t.test(Mean.Spectral.Counts ~ Integrin.Subunit, data = B5vsB1, alternative = .sided, paired = FALSE)$p.value T_test_B5vsB1

[1] 0.0001080229

Integrin intensity responses to Vitronection concentration in talin-positive and talin-negative adhesions

Boxplot of Integrin intensity vs Vitronection concentration in talin-positive and talin-negative adhesions

rr #define panel_file_name b5_Intensity_File_Name <- 1G Integrin b5 Intensity
names(b5.int.vs.VN.conc) <- c(_Pos_vs_Neg, , .integrin.b5.Intensity) b5.int.vs.VN.conc\(Talin_Pos_vs_Neg <- recode(b5.int.vs.VN.conc\)Talin_Pos_vs_Neg, Atypical = _Neg) b5.int.vs.VN.conc\(Talin_Pos_vs_Neg <- recode(b5.int.vs.VN.conc\)Talin_Pos_vs_Neg, typical = _Pos) b5.int.vs.VN.conc\(Group <- paste(b5.int.vs.VN.conc\)Talin_Pos_vs_Neg, b5.int.vs.VN.conc\(Condition, sep = \_\) b5.int.vs.VN.conc\)Group <- factor(b5.int.vs.VN.conc$Group, levels = c(_Pos_1VN, _Neg_1VN, _Pos_3VN, _Neg_3VN, _Pos_10VN, _Neg_10VN)) ggplot(data = b5.int.vs.VN.conc, aes(x = Group, y = Mean.integrin.b5.Intensity)) + geom_boxplot(aes(fill = Talin_Pos_vs_Neg, ymin=..lower.., ymax=..upper..), outlier.shape=NA, notch = TRUE) + scale_fill_manual(values = c(_Pos = #4F80BC, _Neg = #C0504F)) + theme_classic() + coord_cartesian(ylim = c(500, 1300)) + theme(axis.text.x = element_text(angle = 45, hjust = 1)) svglite(paste(panel_output_path, b5_Intensity_File_Name, .svg, sep = \), width=5, height=8) r ggplot(data = b5.int.vs.VN.conc, aes(x = Group, y = Mean.integrin.b5.Intensity)) + geom_boxplot(aes(fill = Talin_Pos_vs_Neg, ymin=..lower.., ymax=..upper..), outlier.shape=NA, notch = TRUE) + scale_fill_manual(values = c(_Pos = #4F80BC, _Neg = #C0504F)) + theme_classic() + coord_cartesian(ylim = c(500, 1300)) + theme(axis.text.x = element_text(angle = 45, hjust = 1)) dev.off()

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Wilcoxon Rank Sum testing of b5 intensity changes given increased Vitronectin

rr TalinPos = subset(b5.int.vs.VN.conc, b5.int.vs.VN.conc\(Talin_Pos_vs_Neg == \Talin_Pos\) TalinPos_VN1_vs_VN3 <- subset(b5.int.vs.VN.conc, b5.int.vs.VN.conc\)Talin_Pos_vs_Neg == _Pos & b5.int.vs.VN.conc\(Condition == \1VN\ | b5.int.vs.VN.conc\)Talin_Pos_vs_Neg == _Pos & b5.int.vs.VN.conc\(Condition == \3VN\ ) TalinPos_VN3_vs_VN10 <- subset(b5.int.vs.VN.conc, b5.int.vs.VN.conc\)Talin_Pos_vs_Neg == _Pos & b5.int.vs.VN.conc\(Condition == \3VN\ | b5.int.vs.VN.conc\)Talin_Pos_vs_Neg == _Pos & b5.int.vs.VN.conc\(Condition == \10VN\ ) Mu_Test_TalinPos_VN1_vs_VN3 = wilcox.test(Mean.integrin.b5.Intensity ~ Condition, data = TalinPos_VN1_vs_VN3)\)p.value Mu_Test_TalinPos_VN3_vs_VN10 = wilcox.test(Mean.integrin.b5.Intensity ~ Condition, data = TalinPos_VN3_vs_VN10)\(p.value TalinNeg_VN1_vs_VN3 <- subset(b5.int.vs.VN.conc, b5.int.vs.VN.conc\)Talin_Pos_vs_Neg == _Neg & b5.int.vs.VN.conc\(Condition == \1VN\ | b5.int.vs.VN.conc\)Talin_Pos_vs_Neg == _Neg & b5.int.vs.VN.conc\(Condition == \3VN\ ) TalinNeg_VN3_vs_VN10 <- subset(b5.int.vs.VN.conc, b5.int.vs.VN.conc\)Talin_Pos_vs_Neg == _Neg & b5.int.vs.VN.conc\(Condition == \3VN\ | b5.int.vs.VN.conc\)Talin_Pos_vs_Neg == _Neg & b5.int.vs.VN.conc\(Condition == \10VN\ ) Mu_Test_TalinNeg_VN1_vs_VN3 = wilcox.test(Mean.integrin.b5.Intensity ~ Condition, data = TalinNeg_VN1_vs_VN3)\)p.value Mu_Test_TalinNeg_VN3_vs_VN10 = wilcox.test(Mean.integrin.b5.Intensity ~ Condition, data = TalinNeg_VN3_vs_VN10)$p.value

Figure 2

Import Data for Figure 2

rr # Set your path to input data data_input_path <- /Users/johnlock/Dropbox/A-CMAC Manchester collaboration/NCB submission documents/Resubmission 2/Source_Data
data_file <- Table 1 Statistics Source Data.xlsx
# Import individual data files FRAP.data <- read_xlsx(paste(data_input_path, data_file, sep = /), sheet = 2S_T, col_names = TRUE) STORM.Data <- read_xlsx(paste(data_input_path, data_file, sep = /), sheet = 2V_W, col_names = TRUE)

Analysis of integrin b5 FRAP recovery curves in Reticular and Focal adhesions

Integrin b5 FRAP analysis comparing Reticular and Focal adhesions

rr #define panel_file_name FRAP_Curve_File_Name <- 2S b5 FRAP recovery
names(FRAP.data) <- c(_b5_FRAP_recovery_relative_to_prebleach, , _ROI, _vs_Focal) FRAP.data\(Reticular_vs_Focal <- recode(FRAP.data\)Reticular_vs_Focal, atypical = ) FRAP.data\(Reticular_vs_Focal <- recode(FRAP.data\)Reticular_vs_Focal, typical = ) ggplot(data=FRAP.data, aes(x=Time, y=Standardised_b5_FRAP_recovery_relative_to_prebleach, colour = Reticular_vs_Focal), alpha = 0.1) + stat_summary(fun.data =_cl_normal, alpha = 0.5) + theme_classic() + scale_colour_manual(values = c( = #BF4C49,  = #4B7FBB)) + geom_smooth(method = , span = 0.2, size = 1, fill = #3A3A3A) svglite(paste(panel_output_path, FRAP_Curve_File_Name, .svg, sep = \), width=6, height=4) r ggplot(data=FRAP.data, aes(x=Time, y=Standardised_b5_FRAP_recovery_relative_to_prebleach, colour = Reticular_vs_Focal), alpha = 0.1) + stat_summary(fun.data =_cl_normal, alpha = 0.5) + theme_classic() + scale_colour_manual(values = c( = #BF4C49,  = #4B7FBB)) + geom_smooth(method = , span = 0.2, size = 1, fill = #3A3A3A) dev.off()

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KS test of FRAP recovery curves

rr Reticular.FRAP.data <- subset(FRAP.data, Reticular_vs_Focal == ) Focal.FRAP.data <- subset(FRAP.data, Reticular_vs_Focal == ) Reticular.FRAP.data.LOESS <- loess(Standardised_b5_FRAP_recovery_relative_to_prebleach ~ Time, data = Reticular.FRAP.data, span = 0.2) Focal.FRAP.data.LOESS <- loess(Standardised_b5_FRAP_recovery_relative_to_prebleach ~ Time, data = Focal.FRAP.data, span = 0.2) Reticular.FRAP.data.LOESS.singular <- unique(Reticular.FRAP.data.LOESS\(fitted) Focal.FRAP.data.LOESS.singular <- unique(Focal.FRAP.data.LOESS\)fitted) KS.Reticular.vs.Focal.FRAP <- ks.test(Reticular.FRAP.data.LOESS.singular, Focal.FRAP.data.LOESS.singular, alternative = .sided) print(KS.Reticular.vs.Focal.FRAP$p.value)

[1] 0.002107921

Analysis of interphase STORM data

STORM data wrangling1

rr ## Determine CMAC.type (Reticular vs Focal depending on presence of A in \(File (column)) CMAC.type = c() for (cluster in 1:length(STORM.Data\)File) ) { CMAC.type[cluster] <- if(grepl(, STORM.Data\(File[cluster]) == TRUE) { \Reticular\ } else if(grepl(\T\, STORM.Data\)File[cluster]) == TRUE) {
} else if(grepl(, STORM.Data\(File[cluster]) == TRUE) { \Non-Retraction\ } else { \Retraction\ } } STORM.Data\)CMAC.type <- cbind(as.character(CMAC.type)) ## Unique nanocluster ID generation = merge of Folder, File and Cluster # STORM.Data\(Unique.ncID <- as.factor(paste(STORM.Data\)Folder, STORM.Data\(File, STORM.Data\)Cluster)) STORM.Data\(Unique.CMACID <- as.factor(paste(STORM.Data\)Folder, STORM.Data$File)) STORM.Data <- as.data.frame(STORM.Data)

Nearest Neighbour Analysis between localisations within unique nanoclusters

rr ## NND analysis of Incite3 data NNND <- c() for (i in 1:length(unique(STORM.Data\(Unique.CMACID)) ) { temp = STORM.Data[STORM.Data\)Unique.CMACID == unique(STORM.Data$Unique.CMACID)[i], ] NNND = append(NNND, nndist(temp[,7], temp[,8], k=1)) }

NAs introduced by coercionNAs introduced by coercion

rr STORM.Data$NNND <- cbind(NNND)

Boxplots summarising Nearest Neighbout Distance per nanocluster

rr NNND.boxplot.filename <- 2V nearest neighbour boxplot
NNND.A = subset(STORM.Data\(NNND, CMAC.type == \Reticular\) NNND.T = subset(STORM.Data\)NNND, CMAC.type == ) svglite(paste(panel_output_path, NNND.boxplot.filename, .svg, sep = \), width=6, height=4) boxplot(NNND ~ CMAC.type, subset(STORM.Data, CMAC.type ==  | CMAC.type == ), notch = TRUE, outline = FALSE, main = Neighbour Distance of Nanocluster by CMAC Type, xlab = type, ylab = Neighbour Distance (nm), plot =
) dev.off()

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rr boxplot(NNND ~ CMAC.type, subset(STORM.Data, CMAC.type ==  | CMAC.type == ), notch = TRUE, outline = FALSE, main = Neighbour Distance of Nanocluster by CMAC Type, xlab = type, ylab = Neighbour Distance (nm), plot =
)

Boxplots summarising Molecular Localization per nanocluster

rr Molecule.number.boxplot.filename <- 2W molecular number boxplot
Molecules.A = as.numeric(subset(STORM.Data\(Molecules, CMAC.type == \Reticular\)) Molecules.T = as.numeric(subset(STORM.Data\)Molecules, CMAC.type == )) svglite(paste(panel_output_path, Molecule.number.boxplot.filename, .svg, sep = \), width=6, height=4) boxplot(as.numeric(Molecules) ~ CMAC.type, subset(STORM.Data, CMAC.type ==  | CMAC.type == ), notch = TRUE, outline = FALSE, main = of Integrin avb5 Molecules by CMAC Type, xlab = type, ylab = of Integrin avb5 Molecules, plot =
) dev.off()

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rr boxplot(as.numeric(Molecules) ~ CMAC.type, subset(STORM.Data, CMAC.type ==  | CMAC.type == ), notch = TRUE, outline = FALSE, main = of Integrin avb5 Molecules by CMAC Type, xlab = type, ylab = of Integrin avb5 Molecules, plot =
)

Figure 3

Import Data for Figure 3

rr # Set your path to input data data_input_path <- /Users/johnlock/Dropbox/A-CMAC Manchester collaboration/NCB submission documents/Resubmission 2/Source_Data
data_file <- Table 1 Statistics Source Data.xlsx
# Import individual data files CytoD2h_vs_control <- read_xlsx(paste(data_input_path, data_file, sep = /), sheet = 3C, col_names = TRUE) CytoD2h_vs_control <- CytoD2h_vs_control[,1:3] Adhesion.Assay.Data <- read_xlsx(paste(data_input_path, data_file, sep = /), sheet = 3E, col_names = TRUE) Adhesion.Assay.Data <- Adhesion.Assay.Data[,1:4] Talin.KD.Data <- read_xlsx(paste(data_input_path, data_file, sep = /), sheet = 3H_I, col_names = TRUE)

Adhesion Assay

Fig 3E Plot Adhesion Levels per Condition

rr oldpar <- par() par(mar = c(10, 2.5, 1, 1)) #define panel_file_name Adhesion_Assay_panel_file_name <- 3E Adhesion Assay Boxplots
Adhesion.Assay.Data\(Condition <- factor(Adhesion.Assay.Data\)Condition, unique(Adhesion.Assay.Data\(Condition)) max_adhesion <- max(Adhesion.Assay.Data\)NumberObjects) Adhesion.Assay.Data\(Normalised_Adhesion <- Adhesion.Assay.Data\)NumberObjects/max_adhesion svglite(paste(panel_output_path, Adhesion_Assay_panel_file_name, .svg, sep = \), width=7.7, height=6.5) boxplot(Normalised_Adhesion ~ Condition, data = Adhesion.Assay.Data, notch = FALSE, varwidth = FALSE, outline = F, las=2, ylab = Cell Number, col = c(1wt -Cyto = #CB2327, 1wt +Cyto +RGD = #CB2327, 1b5 -Cyto = #4775A3, 1b5 -Cyto +RAD = #4775A3, 1b5 -Cyto +RGD = #9EC0E5, 1b5 +Cyto = #4775A3, 1b5 +Cyto +RAD = #4775A3, 1b5 +Cyto +RGD = #9EC0E5), ylim = c(0,1.2)) beeswarm(Normalised_Adhesion ~ Condition, data = Adhesion.Assay.Data, method = ‘center’, add = T, col = , pch = 19, cex = 0.7) dev.off()

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rr boxplot(Normalised_Adhesion ~ Condition, data = Adhesion.Assay.Data, notch = FALSE, varwidth = FALSE, outline = F, las=2, ylab = Cell Number, col = c(1wt -Cyto = #CB2327, 1wt +Cyto +RGD = #CB2327, 1b5 -Cyto = #4775A3, 1b5 -Cyto +RAD = #4775A3, 1b5 -Cyto +RGD = #9EC0E5, 1b5 +Cyto = #4775A3, 1b5 +Cyto +RAD = #4775A3, 1b5 +Cyto +RGD = #9EC0E5), ylim = c(0,1.2)) beeswarm(Normalised_Adhesion ~ Condition, data = Adhesion.Assay.Data, method = ‘center’, add = T, col = , pch = 19, cex = 0.7)

T-testing for Adhesion Level differences with Holm-Bonferroni p-value correction

rr p_values <- c() for (i in 1:length(unique(Adhesion.Assay.Data\(Condition))){ for (j in 1:length(unique(Adhesion.Assay.Data\)Condition))){ Condition_1 = subset(Adhesion.Assay.Data, Adhesion.Assay.Data\(Condition == unique(Adhesion.Assay.Data\)Condition)[i]) Condition_1\(Condition_Number = as.factor(i) Condition_2 = subset(Adhesion.Assay.Data, Adhesion.Assay.Data\)Condition == unique(Adhesion.Assay.Data\(Condition)[j]) Condition_2\)Condition_Number = as.factor(j+100) Condition_Pair = rbind(Condition_1, Condition_2) Condition_Pair_ttest = t.test(Normalised_Adhesion ~ Condition_Number, data = Condition_Pair, paired = FALSE)\(p.value p_values = rbind(p_values, Condition_Pair_ttest) } } p_values <- as.vector(p_values) p_value_matrix <- matrix(p_values,nrow = length(unique(Adhesion.Assay.Data\)Condition)),ncol = length(unique(Adhesion.Assay.Data\(Condition))) columns <- as.vector(unique(Adhesion.Assay.Data\)Condition)) rows <- as.vector(unique(Adhesion.Assay.Data\(Condition)) p_value_dataframe <- as.data.frame(p_value_matrix, row.names = rows) names(p_value_dataframe) <- columns # print(p_value_dataframe) # Introduce Holm (also called Holm-Bonferroni) p-value correction (used because it gives same stringency against false positives (type 1 errors) with lower probability of false negatives (type 2 errors))) corrected_p_values <- p.adjust(p_values, method = \holm\, n = length(p_values)) corrected_p_value_matrix1 <- matrix(corrected_p_values,nrow = length(unique(Adhesion.Assay.Data\)Condition)),ncol = length(unique(Adhesion.Assay.Data\(Condition))) columns <- as.vector(unique(Adhesion.Assay.Data\)Condition)) rows <- as.vector(unique(Adhesion.Assay.Data$Condition)) corrected_p_value_dataframe <- as.data.frame(corrected_p_value_matrix1, row.names = rows) names(corrected_p_value_dataframe) <- columns print(corrected_p_value_dataframe)

Plot heatmap of corrected p_values

rr heatmap.2(log(corrected_p_value_matrix1), dendrogram = ‘none’, Rowv = FALSE, Colv = FALSE, labRow = rows, labCol = columns, srtCol = 45, cexRow = 1, cexCol = 1, margins = c(7, 9), symm = TRUE, revC = FALSE, breaks = c(log(1e-300), log(1e-10), log(1e-6), log(0.001), log(0.01), log(0.05), log(1)), rowsep = 1:nrow(corrected_p_value_matrix1), colsep = 1:ncol(corrected_p_value_matrix1), sepcolor = ‘darkgrey’, sepwidth = c(0.02,0.02), trace = ‘none’, col = c(, , , , , ), denscol = NULL, keysize = 1.5, key.title = NA)

Wilcoxon Rank Sum testing for Adhesion Level differences

rr U_values <- c() for (i in 1:length(unique(Adhesion.Assay.Data\(Condition))){ for (j in 1:length(unique(Adhesion.Assay.Data\)Condition))){ Condition_1 = subset(Adhesion.Assay.Data, Adhesion.Assay.Data\(Condition == unique(Adhesion.Assay.Data\)Condition)[i]) Condition_1\(Condition_Number = as.factor(i) Condition_2 = subset(Adhesion.Assay.Data, Adhesion.Assay.Data\)Condition == unique(Adhesion.Assay.Data\(Condition)[j]) Condition_2\)Condition_Number = as.factor(j+100) Condition_Pair = rbind(Condition_1, Condition_2) Condition_Pair_Utest = wilcox.test(Normalised_Adhesion ~ Condition_Number, data = Condition_Pair)$p.value U_values = rbind(p_values, Condition_Pair_Utest) } }

cannot compute exact p-value with tiescannot compute exact p-value with tiescannot compute exact p-value with tiescannot compute exact p-value with tiescannot compute exact p-value with tiescannot compute exact p-value with tiescannot compute exact p-value with tiescannot compute exact p-value with tiescannot compute exact p-value with tiescannot compute exact p-value with tiescannot compute exact p-value with tiescannot compute exact p-value with tiescannot compute exact p-value with tiescannot compute exact p-value with tiescannot compute exact p-value with tiescannot compute exact p-value with tiescannot compute exact p-value with tiescannot compute exact p-value with tiescannot compute exact p-value with tiescannot compute exact p-value with tiescannot compute exact p-value with tiescannot compute exact p-value with tiescannot compute exact p-value with tiescannot compute exact p-value with tiescannot compute exact p-value with tiescannot compute exact p-value with tiescannot compute exact p-value with tiescannot compute exact p-value with tiescannot compute exact p-value with tiescannot compute exact p-value with tiescannot compute exact p-value with tiescannot compute exact p-value with tiescannot compute exact p-value with tiescannot compute exact p-value with tiescannot compute exact p-value with tiescannot compute exact p-value with tiescannot compute exact p-value with tiescannot compute exact p-value with ties

rr U_values <- as.vector(U_values) U_value_matrix <- matrix(U_values, nrow = length(unique(Adhesion.Assay.Data\(Condition)),ncol = length(unique(Adhesion.Assay.Data\)Condition))) columns <- as.vector(unique(Adhesion.Assay.Data\(Condition)) rows <- as.vector(unique(Adhesion.Assay.Data\)Condition)) U_value_dataframe <- as.data.frame(U_value_matrix, row.names = rows) names(U_value_dataframe) <- columns print(U_value_dataframe)

Talin KD effects

Plot and test Talin KD effects on cell area and integrin b5 intensity within adhesions

rr # beeswarm(Total.Talin.per.Cell ~ siRNA..Oligo, data = Talin.KD.Data, method = ‘center’, col = , pch = 19, cex = 1, ylim = c(0,1)) # # beeswarm(Cell.Area ~ siRNA..Oligo, data = Talin.KD.Data, method = ‘center’, col = , pch = 19, cex = 1, ylim = c(0,1)) # # beeswarm(Mean.Clustered.b5.per.Cell ~ siRNA..Oligo, data = Talin.KD.Data, method = ‘center’, col = , pch = 19, cex = 1)#, ylim = c(0,1)) plot(log2(Talin.KD.Data\(Total.Talin.per.Cell), Talin.KD.Data\)Cell.Area, ylim = c(0,1), pch = c(0, 0, 0, 0, 2, 21), col = c(, , , , , )) abline(lm(Talin.KD.Data\(Cell.Area ~ log2(Talin.KD.Data\)Total.Talin.per.Cell)), col = ) r lm.cell.area_vs_talin.per.cell <- lm(Talin.KD.Data\(Cell.Area ~ log2(Talin.KD.Data\)Total.Talin.per.Cell)) TalinKD_Cell_Area_panel_file_name <- 3H TalinKD_vs_Cell_Area
svglite(paste(panel_output_path, TalinKD_Cell_Area_panel_file_name, .svg, sep = \), width=3, height=3) plot(log2(Talin.KD.Data\(Total.Talin.per.Cell), Talin.KD.Data\)Cell.Area, ylim = c(0,1), pch = c(0, 0, 0, 0, 2, 21), col = c(, , , , , ), cex.axis = 0.5, cex.label = 0.5) r abline(lm(Talin.KD.Data\(Cell.Area ~ log2(Talin.KD.Data\)Total.Talin.per.Cell)), col = ) dev.off()

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rr plot(Talin.KD.Data\(Total.Talin.per.Cell, log2(Talin.KD.Data\)Mean.Clustered.b5.per.Cell), xlim = c(0,1), ylim = c(0,4), pch = c(0, 0, 0, 0, 2, 21), col = c(, , , , , )) abline(lm(log2(Talin.KD.Data\(Mean.Clustered.b5.per.Cell) ~ Talin.KD.Data\)Total.Talin.per.Cell), col = ) r lm.b5.intensity_vs_talin.per.cell <- lm(log2(Talin.KD.Data\(Mean.Clustered.b5.per.Cell) ~ Talin.KD.Data\)Total.Talin.per.Cell) TalinKD_b5_Intensity_panel_file_name <- 3I TalinKD_vs_b5_Intensity
svglite(paste(panel_output_path, TalinKD_b5_Intensity_panel_file_name, .svg, sep = \), width=3, height=3) plot(Talin.KD.Data\(Total.Talin.per.Cell, log2(Talin.KD.Data\)Mean.Clustered.b5.per.Cell), xlim = c(0,1), ylim = c(0,4), pch = c(0, 0, 0, 0, 2, 21), col = c(, , , , , ), cex.axis = 0.5, cex.label = 0.5) r abline(lm(log2(Talin.KD.Data\(Mean.Clustered.b5.per.Cell) ~ Talin.KD.Data\)Total.Talin.per.Cell), col = ) dev.off()

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Test CytoD effects on adhesion intensities

Wilcoxon rank sum test of CytoD effect on vinculin and b5 intensity in adhesions

rr names(CytoD2h_vs_control) <- c(_Intensity, 5_Intensity, ) Vinculin_Intensity_mu_test <- wilcox.test(Vinculin_Intensity ~ Condition, data = CytoD2h_vs_control)$p.value print(Vinculin_Intensity_mu_test)

[1] 1.785123e-166

rr b5_Intensity_mu_test <- wilcox.test(b5_Intensity ~ Condition, data = CytoD2h_vs_control)$p.value print(b5_Intensity_mu_test)

[1] 1.624755e-99

Figure 5

Import Data for Figure 5

rr # Set your path to input data data_input_path <- /Users/johnlock/Dropbox/A-CMAC Manchester collaboration/NCB submission documents/Resubmission 2/Source_Data
data_file <- Table 1 Statistics Source Data.xlsx
# Import individual data files Primary.siRNA.Screen.Data <- read_xlsx(paste(data_input_path, data_file, sep = /), sheet = 5B_C, col_names = TRUE) Neo.LY.Drug.Treatment.Data <- read_xlsx(paste(data_input_path, data_file, sep = /), sheet = 5D, col_names = TRUE)

Analyse Primary siRNA Screen responses

Boxplot changes in reticular to focal adhesion b5 intensity ratio due to siRNA

rr siRNA_boxplot_summary_filename <- 5B siRNA boxplot summary
cell.numbers.primary.screen <- count(Primary.siRNA.Screen.Data, siRNA_Target) count(Primary.siRNA.Screen.Data, siRNA_Target) r mean.cell.numbers.primary.screen <- summarise(cell.numbers.primary.screen, avg = mean(n), SD = sd(n), sum(n)) summarise(cell.numbers.primary.screen, avg = mean(n), SD = sd(n), sum(n))

rr ggplot(Primary.siRNA.Screen.Data, aes(x=siRNA_Target, y=Ratio_A.CMAC.to.T.CMAC_mean_intensity.z.score, color = factor(siRNA_Treatment_Type))) + geom_boxplot(notch = TRUE, outlier.colour = ) + scale_x_discrete(limits=c(,,_3Ctrl_Pool, -TARGETplus Non-targeting Control, Non-targeting Control, , 5, , 4KA, 4K2B, , 5K1B, 5K1C, 3C2A)) + theme(axis.text.x = element_text(angle=90)) + ylim(-3, 6)#+ svglite(paste(panel_output_path, siRNA_boxplot_summary_filename, .svg, sep = \), width=7, height=5) r ggplot(Primary.siRNA.Screen.Data, aes(x=siRNA_Target, y=Ratio_A.CMAC.to.T.CMAC_mean_intensity.z.score, color = factor(siRNA_Treatment_Type))) + geom_boxplot(notch = TRUE, outlier.colour = ) + scale_x_discrete(limits=c(,,_3Ctrl_Pool, -TARGETplus Non-targeting Control, Non-targeting Control, , 5, , 4KA, 4K2B, , 5K1B, 5K1C, 3C2A)) + theme(axis.text.x = element_text(angle=90)) + ylim(-3, 6)#+ dev.off()

quartz_off_screen 
                2 

Parallel coordinates plot of adhesion responses to siRNA

rr siRNA_parcoord_summary_filename <- 5C siRNA parcoord summary
Primary_screen_siRNA_subset <- subset(Primary.siRNA.Screen.Data, siRNA_Target ==  | siRNA_Target ==  | siRNA_Target == _3Ctrl_Pool | siRNA_Target == -TARGETplus Non-targeting Control | siRNA_Target == Non-targeting Control | siRNA_Target ==  | siRNA_Target == 4KA | siRNA_Target == 4K2B | siRNA_Target ==  | siRNA_Target == 5K1B | siRNA_Target == 5K1C | siRNA_Target == 3C2A) Primary_screen_siRNA_subset_grouped <- aggregate(Primary_screen_siRNA_subset[, c(11,13,18)], list(Primary_screen_siRNA_subset\(siRNA_Target), median) Primary_screen_siRNA_subset_grouped\)Group.1 <- as.factor(Primary_screen_siRNA_subset_grouped$Group.1) names(Primary_screen_siRNA_subset_grouped) <- c(_Target, _Reticular_Adhesion_Intensity, _Focal_Adhesion_Intensity, _Reticular_Focal_Adhesion_Intensity) svglite(paste(panel_output_path, siRNA_parcoord_summary_filename, .svg, sep = \), width=7, height=5) ggparcoord(Primary_screen_siRNA_subset_grouped, columns = c(2,3,4), groupColumn = 1, scale = , order = c(2,3,4)) + theme(axis.text.x = element_text(angle=60, hjust = 1, size = 5)) dev.off()

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rr ggparcoord(Primary_screen_siRNA_subset_grouped, columns = c(2,3,4), groupColumn = 1, scale = , order = c(2,3,4)) + theme(axis.text.x = element_text(angle=60, hjust = 1, size = 5))

Mann-Whitney test for significant siRNA treatment effects

rr siRNA.conditions.for.U.test <- subset(Primary.siRNA.Screen.Data, siRNA_Target !=  & siRNA_Target !=  & siRNA_Target != _3Ctrl_Pool & siRNA_Target != Non-targeting Control) p_values <- c() for (i in 1:length(unique(siRNA.conditions.for.U.test\(siRNA_Target))){ for (j in 1:length(unique(siRNA.conditions.for.U.test\)siRNA_Target))){ siRNA_Target_1 = subset(siRNA.conditions.for.U.test, siRNA.conditions.for.U.test\(siRNA_Target == unique(siRNA.conditions.for.U.test\)siRNA_Target)[i]) siRNA_Target_1\(siRNA_Target_Number = as.factor(i) siRNA_Target_2 = subset(siRNA.conditions.for.U.test, siRNA.conditions.for.U.test\)siRNA_Target == unique(siRNA.conditions.for.U.test\(siRNA_Target)[j]) siRNA_Target_2\)siRNA_Target_Number = as.factor(j+100) siRNA_Target_Pair = rbind(siRNA_Target_1, siRNA_Target_2) siRNA_Target_Pair_ttest = wilcox.test(Ratio_A.CMAC.to.T.CMAC_mean_intensity.z.score ~ siRNA_Target_Number, data = siRNA_Target_Pair)\(p.value p_values = rbind(p_values, siRNA_Target_Pair_ttest) } } p_values <- as.vector(p_values) p_value_matrix <- matrix(p_values,nrow = length(unique(siRNA.conditions.for.U.test\)siRNA_Target)),ncol = length(unique(siRNA.conditions.for.U.test\(siRNA_Target))) columns <- as.vector(unique(siRNA.conditions.for.U.test\)siRNA_Target)) rows <- as.vector(unique(siRNA.conditions.for.U.test\(siRNA_Target)) p_value_dataframe <- as.data.frame(p_value_matrix, row.names = rows) names(p_value_dataframe) <- columns # print(p_value_dataframe) # Introduce Holm (also called Holm-Bonferroni) p-value correction (used because it gives same stringency against false positives (type 1 errors) with lower probability of false negatives (type 2 errors))) corrected_p_values <- p.adjust(p_values, method = \holm\, n = length(p_values)) corrected_p_value_matrix2 <- matrix(corrected_p_values,nrow = length(unique(siRNA.conditions.for.U.test\)siRNA_Target)),ncol = length(unique(siRNA.conditions.for.U.test\(siRNA_Target))) columns <- as.vector(unique(siRNA.conditions.for.U.test\)siRNA_Target)) rows <- as.vector(unique(siRNA.conditions.for.U.test$siRNA_Target)) corrected_p_value_dataframe <- as.data.frame(corrected_p_value_matrix2, row.names = rows) names(corrected_p_value_dataframe) <- columns print(corrected_p_value_dataframe)

Plot heatmap of corrected p_values mann-whitney

rr heatmap.2(log(corrected_p_value_matrix2), dendrogram = ‘none’, Rowv = FALSE, Colv = FALSE, labRow = rows, labCol = columns, srtCol = 45, cexRow = 1, cexCol = 1, margins = c(7, 9), symm = TRUE, revC = FALSE, breaks = c(log(1e-300), log(1e-50), log(1e-10), log(1e-6), log(0.001), log(0.01), log(0.05), log(1)), rowsep = 1:nrow(corrected_p_value_matrix2), colsep = 1:ncol(corrected_p_value_matrix2), sepcolor = ‘darkgrey’, sepwidth = c(0.02,0.02), trace = ‘none’, col = c(, , , , , , ), denscol = NULL, keysize = 1.5, key.title = NA)

Analyse Neomycin and LY drug treatment responses

Boxplots of b5 adhesion intensity changes due to neomycin or LY

rr PIP_drug_boxplot_summary_filename <- 5D PIP drug boxplot summary
cell.numbers.NEO_LY.screen <- count(Neo.LY.Drug.Treatment.Data, Drug) count(Neo.LY.Drug.Treatment.Data, Drug) r mean.cell.numbers.NEO_LY.screen <- summarise(cell.numbers.NEO_LY.screen, avg = mean(n), SD = sd(n), sum(n)) summarise(cell.numbers.NEO_LY.screen, avg = mean(n), SD = sd(n), sum(n))

rr svglite(paste(panel_output_path, PIP_drug_boxplot_summary_filename, .svg, sep = \), width=7, height=5) p1 <- ggplot(Neo.LY.Drug.Treatment.Data, aes(x=Drug, y=Median.Mean..z.score, color = factor(Drug))) + geom_boxplot(notch = TRUE, outlier.colour = ) + scale_x_discrete(limits=c(, _10mM,_25mM)) + theme(axis.text.x = element_text(angle=45, vjust = 0.5)) + coord_cartesian(ylim=c(-3, 6)) + ylab(Reticular Adhesion Intensity) + theme(legend.position=) ## plot T-CMAC intensity Z-score ggplot2 boxplot - per Drug p2 <- ggplot(Neo.LY.Drug.Treatment.Data, aes(x=Drug, y=Median.Mean._T.CMACs.z.score, color = factor(Drug))) + geom_boxplot(notch = TRUE, outlier.colour = ) + scale_x_discrete(limits=c(, _10mM,_25mM)) + theme(axis.text.x = element_text(angle=45, vjust = 0.5)) + coord_cartesian(ylim=c(-3, 6)) + ylab(Focal Adhesion Intensity) + theme(legend.position=) ## plot intensity ratio Z-score ggplot2 boxplot - per Drug p3 <- ggplot(Neo.LY.Drug.Treatment.Data, aes(x=Drug, y=Ratio_A.CMAC.to.T.CMAC_mean_intensity.z.score, color = factor(Drug))) + geom_boxplot(notch = TRUE, outlier.colour = ) + scale_x_discrete(limits=c(, _10mM,_25mM)) + theme(axis.text.x = element_text(angle=45, vjust = 0.5)) + coord_cartesian(ylim=c(-3, 6)) + ylab(Reticular:Focal Adhesion Intensity) + theme(legend.position=) grid.arrange(p1, p2, p3, ncol = 3) dev.off()

null device 
          1 

rr p1 <- ggplot(Neo.LY.Drug.Treatment.Data, aes(x=Drug, y=Median.Mean..z.score, color = factor(Drug))) + geom_boxplot(notch = TRUE, outlier.colour = ) + scale_x_discrete(limits=c(, _10mM,_25mM)) + theme(axis.text.x = element_text(angle=45, vjust = 0.5)) + coord_cartesian(ylim=c(-3, 6)) + ylab(Reticular Adhesion Intensity) + theme(legend.position=) ## plot T-CMAC intensity Z-score ggplot2 boxplot - per Drug p2 <- ggplot(Neo.LY.Drug.Treatment.Data, aes(x=Drug, y=Median.Mean._T.CMACs.z.score, color = factor(Drug))) + geom_boxplot(notch = TRUE, outlier.colour = ) + scale_x_discrete(limits=c(, _10mM,_25mM)) + theme(axis.text.x = element_text(angle=45, vjust = 0.5)) + coord_cartesian(ylim=c(-3, 6)) + ylab(Focal Adhesion Intensity) + theme(legend.position=) ## plot intensity ratio Z-score ggplot2 boxplot - per Drug p3 <- ggplot(Neo.LY.Drug.Treatment.Data, aes(x=Drug, y=Ratio_A.CMAC.to.T.CMAC_mean_intensity.z.score, color = factor(Drug))) + geom_boxplot(notch = TRUE, outlier.colour = ) + scale_x_discrete(limits=c(, _10mM,_25mM)) + theme(axis.text.x = element_text(angle=45, vjust = 0.5)) + coord_cartesian(ylim=c(-3, 6)) + ylab(Reticular:Focal Adhesion Intensity) + theme(legend.position=) grid.arrange(p1, p2, p3, ncol = 3)

Mann-Whitney test for significant Drug treatment effects on Reticular adhesion intensity

rr p_values <- c() for (i in 1:length(unique(Neo.LY.Drug.Treatment.Data\(Drug))){ for (j in 1:length(unique(Neo.LY.Drug.Treatment.Data\)Drug))){ Drug_1 = subset(Neo.LY.Drug.Treatment.Data, Neo.LY.Drug.Treatment.Data\(Drug == unique(Neo.LY.Drug.Treatment.Data\)Drug)[i]) Drug_1\(Drug_Number = as.factor(i) Drug_2 = subset(Neo.LY.Drug.Treatment.Data, Neo.LY.Drug.Treatment.Data\)Drug == unique(Neo.LY.Drug.Treatment.Data\(Drug)[j]) Drug_2\)Drug_Number = as.factor(j+100) Drug_Pair = rbind(Drug_1, Drug_2) Drug_Pair_ttest = wilcox.test(Median.Mean..z.score ~ Drug_Number, data = Drug_Pair)\(p.value p_values = rbind(p_values, Drug_Pair_ttest) } } p_values <- as.vector(p_values) p_value_matrix <- matrix(p_values,nrow = length(unique(Neo.LY.Drug.Treatment.Data\)Drug)),ncol = length(unique(Neo.LY.Drug.Treatment.Data\(Drug))) columns <- as.vector(unique(Neo.LY.Drug.Treatment.Data\)Drug)) rows <- as.vector(unique(Neo.LY.Drug.Treatment.Data\(Drug)) p_value_dataframe <- as.data.frame(p_value_matrix, row.names = rows) names(p_value_dataframe) <- columns # print(p_value_dataframe) # Introduce Holm (also called Holm-Bonferroni) p-value correction (used because it gives same stringency against false positives (type 1 errors) with lower probability of false negatives (type 2 errors))) corrected_p_values <- p.adjust(p_values, method = \holm\, n = length(p_values)) corrected_p_value_matrix3 <- matrix(corrected_p_values,nrow = length(unique(Neo.LY.Drug.Treatment.Data\)Drug)),ncol = length(unique(Neo.LY.Drug.Treatment.Data\(Drug))) columns <- as.vector(unique(Neo.LY.Drug.Treatment.Data\)Drug)) rows <- as.vector(unique(Neo.LY.Drug.Treatment.Data$Drug)) corrected_p_value_dataframe <- as.data.frame(corrected_p_value_matrix3, row.names = rows) names(corrected_p_value_dataframe) <- columns print(corrected_p_value_dataframe)

Mann-Whitney test for significant Drug treatment effects on Focal adhesion intensity

rr p_values <- c() for (i in 1:length(unique(Neo.LY.Drug.Treatment.Data\(Drug))){ for (j in 1:length(unique(Neo.LY.Drug.Treatment.Data\)Drug))){ Drug_1 = subset(Neo.LY.Drug.Treatment.Data, Neo.LY.Drug.Treatment.Data\(Drug == unique(Neo.LY.Drug.Treatment.Data\)Drug)[i]) Drug_1\(Drug_Number = as.factor(i) Drug_2 = subset(Neo.LY.Drug.Treatment.Data, Neo.LY.Drug.Treatment.Data\)Drug == unique(Neo.LY.Drug.Treatment.Data\(Drug)[j]) Drug_2\)Drug_Number = as.factor(j+100) Drug_Pair = rbind(Drug_1, Drug_2) Drug_Pair_ttest = wilcox.test(Median.Mean._T.CMACs.z.score ~ Drug_Number, data = Drug_Pair)\(p.value p_values = rbind(p_values, Drug_Pair_ttest) } } p_values <- as.vector(p_values) p_value_matrix <- matrix(p_values,nrow = length(unique(Neo.LY.Drug.Treatment.Data\)Drug)),ncol = length(unique(Neo.LY.Drug.Treatment.Data\(Drug))) columns <- as.vector(unique(Neo.LY.Drug.Treatment.Data\)Drug)) rows <- as.vector(unique(Neo.LY.Drug.Treatment.Data\(Drug)) p_value_dataframe <- as.data.frame(p_value_matrix, row.names = rows) names(p_value_dataframe) <- columns # print(p_value_dataframe) # Introduce Holm (also called Holm-Bonferroni) p-value correction (used because it gives same stringency against false positives (type 1 errors) with lower probability of false negatives (type 2 errors))) corrected_p_values <- p.adjust(p_values, method = \holm\, n = length(p_values)) corrected_p_value_matrix4 <- matrix(corrected_p_values,nrow = length(unique(Neo.LY.Drug.Treatment.Data\)Drug)),ncol = length(unique(Neo.LY.Drug.Treatment.Data\(Drug))) columns <- as.vector(unique(Neo.LY.Drug.Treatment.Data\)Drug)) rows <- as.vector(unique(Neo.LY.Drug.Treatment.Data$Drug)) corrected_p_value_dataframe <- as.data.frame(corrected_p_value_matrix4, row.names = rows) names(corrected_p_value_dataframe) <- columns print(corrected_p_value_dataframe)

Mann-Whitney test for significant Drug treatment effects on intensity ratio Z-score

rr p_values <- c() for (i in 1:length(unique(Neo.LY.Drug.Treatment.Data\(Drug))){ for (j in 1:length(unique(Neo.LY.Drug.Treatment.Data\)Drug))){ Drug_1 = subset(Neo.LY.Drug.Treatment.Data, Neo.LY.Drug.Treatment.Data\(Drug == unique(Neo.LY.Drug.Treatment.Data\)Drug)[i]) Drug_1\(Drug_Number = as.factor(i) Drug_2 = subset(Neo.LY.Drug.Treatment.Data, Neo.LY.Drug.Treatment.Data\)Drug == unique(Neo.LY.Drug.Treatment.Data\(Drug)[j]) Drug_2\)Drug_Number = as.factor(j+100) Drug_Pair = rbind(Drug_1, Drug_2) Drug_Pair_ttest = wilcox.test(as.numeric(Ratio_A.CMAC.to.T.CMAC_mean_intensity.z.score) ~ Drug_Number, data = Drug_Pair)\(p.value p_values = rbind(p_values, Drug_Pair_ttest) } } p_values <- as.vector(p_values) p_value_matrix <- matrix(p_values,nrow = length(unique(Neo.LY.Drug.Treatment.Data\)Drug)),ncol = length(unique(Neo.LY.Drug.Treatment.Data\(Drug))) columns <- as.vector(unique(Neo.LY.Drug.Treatment.Data\)Drug)) rows <- as.vector(unique(Neo.LY.Drug.Treatment.Data\(Drug)) p_value_dataframe <- as.data.frame(p_value_matrix, row.names = rows) names(p_value_dataframe) <- columns # print(p_value_dataframe) # Introduce Holm (also called Holm-Bonferroni) p-value correction (used because it gives same stringency against false positives (type 1 errors) with lower probability of false negatives (type 2 errors))) corrected_p_values <- p.adjust(p_values, method = \holm\, n = length(p_values)) corrected_p_value_matrix5 <- matrix(corrected_p_values,nrow = length(unique(Neo.LY.Drug.Treatment.Data\)Drug)),ncol = length(unique(Neo.LY.Drug.Treatment.Data\(Drug))) columns <- as.vector(unique(Neo.LY.Drug.Treatment.Data\)Drug)) rows <- as.vector(unique(Neo.LY.Drug.Treatment.Data$Drug)) corrected_p_value_dataframe <- as.data.frame(corrected_p_value_matrix5, row.names = rows) names(corrected_p_value_dataframe) <- columns print(corrected_p_value_dataframe)

Figure 6

Import Data for Figure 6

rr # Set your path to input data data_input_path <- /Users/johnlock/Dropbox/A-CMAC Manchester collaboration/NCB submission documents/Resubmission 2/Source_Data
data_file <- Table 1 Statistics Source Data.xlsx
# Import individual data files Proliferation <- read_xlsx(paste(data_input_path, data_file, sep = /), sheet = 6A, col_names = TRUE) Proliferation\(Day <- as.factor(Proliferation\)Day) EDU_Incorporation_Percentage <- read_xlsx(paste(data_input_path, data_file, sep = /), sheet = 6B, col_names = TRUE) EDU_Incorporation_Percentage <- EDU_Incorporation_Percentage[,1:2]

Summarise Proliferation data per Condition per Day

rr Prol_Sum <- Proliferation %>% group_by(.dots = c(‘Condition’, ‘Day’)) %>% summarise( mean_proliferation = mean(Relative.Proliferation, na.rm = TRUE), SD_proliferation = sd(Relative.Proliferation, na.rm = TRUE) )

Plot Cell Proliferation Comparison of control vs b5KD

rr proliferation_linechart_filename <- 6A proliferation linechart
# The errorbars overlapped, so use position_dodge to move them horizontally pd <- position_dodge(0.1) # move them .05 to the left and right svglite(paste(panel_output_path, proliferation_linechart_filename, .svg, sep = \), width=4, height=3) ggplot(Prol_Sum, aes(x=Day, y=mean_proliferation, colour=Condition, group=Condition)) + geom_errorbar(aes(ymin=mean_proliferation-SD_proliferation, ymax=mean_proliferation+SD_proliferation), width=.1, position=pd) + scale_color_manual(values = c( = #49494A, 5KD = #EC1C24)) + geom_line(position=pd, size = 1) + geom_point(position=pd, size=3, shape=19) + # 21 is filled circle xlab() + ylab(Proliferation) + theme_light() dev.off()

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rr ggplot(Prol_Sum, aes(x=Day, y=mean_proliferation, colour=Condition, group=Condition)) + geom_errorbar(aes(ymin=mean_proliferation-SD_proliferation, ymax=mean_proliferation+SD_proliferation), width=.1, position=pd) + scale_color_manual(values = c( = #49494A, 5KD = #EC1C24)) + geom_line(position=pd, size = 1) + geom_point(position=pd, size=3, shape=19) + # 21 is filled circle xlab() + ylab(Proliferation) + theme_light()

T-testing Cell Proliferation Comparison per Condition per Day

rr Proliferation\(Day <- as.factor(Proliferation\)Day) p_values <- c() for (i in 1:length(unique(Proliferation$Day))){

single_day_data = subset(Proliferation, Proliferation$Day == unique(Proliferation$Day)[i])

Control_vs_b5KD_per_day_ttest = t.test(Relative.Proliferation ~ Condition, data = single_day_data, paired = FALSE)$p.value
p_values = rbind(p_values, Control_vs_b5KD_per_day_ttest)

} p_values <- as.vector(p_values) p_value_matrix <- matrix(p_values,nrow = 1,ncol = length(p_values)) columns <- as.vector(paste(, unique(Proliferation\(Day), sep = \ \)) rows <- paste(unique(Proliferation\)Condition)[1], unique(Proliferation$Condition)[2], sep = _vs_) p_value_dataframe <- as.data.frame(p_value_matrix, row.names = rows) names(p_value_dataframe) <- columns print(p_value_dataframe)

EDU Incorporation Comparison per Condition

Fig 6B Plot EDU Incorporation Boxplot per Condition

rr #define panel_file_name EDU_boxplot_panel_file_name <- 6B EDU Incorporation Boxplots
EDU_Incorporation_Percentage\(Condition <- factor(EDU_Incorporation_Percentage\)Condition, c(, 5KD)) svglite(paste(panel_output_path, EDU_boxplot_panel_file_name, .svg, sep = \), width=4, height=6) boxplot(Percentage.EDU.positive.Cells.per.image ~ Condition, data = EDU_Incorporation_Percentage, notch = T, varwidth = T, outline = F, ylab = % EDU positive cells, col = c(control = #49494A, b5KD = #EC1C24), ylim = c(0,115)) beeswarm(Percentage.EDU.positive.Cells.per.image ~ Condition, data = EDU_Incorporation_Percentage, method = ‘center’, add = T, col = 2, pch = 1, cex = 1.3) dev.off()

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          1 

rr boxplot(Percentage.EDU.positive.Cells.per.image ~ Condition, data = EDU_Incorporation_Percentage, notch = T, varwidth = T, outline = F, ylab = % EDU positive cells, col = c(control = #49494A, b5KD = #EC1C24), ylim = c(0,115)) r beeswarm(Percentage.EDU.positive.Cells.per.image ~ Condition, data = EDU_Incorporation_Percentage, method = ‘center’, add = T, col = 2, pch = 1, cex = 1.3)

Fig6B Mann-Whitney µ test for EDU Incorporation differences

rr #Note: Equivalent to Wilcoxon rank sum test as data is unpaired Utest.b5KD.vs.control_EDU <- wilcox.test(Percentage.EDU.positive.Cells.per.image ~ Condition, data = EDU_Incorporation_Percentage)

cannot compute exact p-value with ties

rr Mann_Whitney_EDU_Incorporation_Score <- Utest.b5KD.vs.control_EDU$p.value print(Mann_Whitney_EDU_Incorporation_Score)

[1] 0.5337576

Figure 7

Import Data for Figure 7

rr # Set your path to input data data_input_path <- /Users/johnlock/Dropbox/A-CMAC Manchester collaboration/NCB submission documents/Resubmission 2/Source_Data
data_file <- Table 1 Statistics Source Data.xlsx
# Import individual data files STORM.Data <- read_xlsx(paste(data_input_path, data_file, sep = /), sheet = 7E_F, col_names = TRUE)

Analysis of mitotic STORM data

STORM data wrangling2

rr ## Determine CMAC.type (Reticular vs Focal depending on presence of A in \(File (column)) CMAC.type = c() for (cluster in 1:length(STORM.Data\)File) ) { CMAC.type[cluster] <- if(grepl(, STORM.Data\(File[cluster]) == TRUE) { \Reticular\ } else if(grepl(\T\, STORM.Data\)File[cluster]) == TRUE) {
} else if(grepl(, STORM.Data\(File[cluster]) == TRUE) { \Non-Retraction\ } else { \Retraction\ } } STORM.Data\)CMAC.type <- cbind(as.character(CMAC.type)) ## Unique nanocluster ID generation = merge of Folder, File and Cluster STORM.Data\(Unique.ncID <- as.factor(paste(STORM.Data\)Folder, STORM.Data\(File, STORM.Data\)Cluster)) STORM.Data\(Unique.CMACID <- as.factor(paste(STORM.Data\)Folder, STORM.Data$File))

Nearest Neighbour Analysis between localisations within unique nanoclusters2

rr ## NND analysis of Incite3 data NNND <- c() for (i in 1:length(unique(STORM.Data\(Unique.CMACID)) ) { temp = STORM.Data[STORM.Data\)Unique.CMACID == unique(STORM.Data\(Unique.CMACID)[i], ] NNND = append(NNND, nndist(temp\)Centroid.nm., temp$Centroid.nm..1, k=1)) }

NAs introduced by coercionNAs introduced by coercion

rr STORM.Data$NNND <- cbind(NNND)

Boxplots summarising Nearest Neighbout Distance per nanocluster2

rr NNND.A = subset(STORM.Data\(NNND, CMAC.type == \Non-Retraction\) NNND.T = subset(STORM.Data\)NNND, CMAC.type == ) TEST_nnnd_NRvsR <- wilcox.test(NNND.A, NNND.T, paired = FALSE) NNND_boxplot_filename <- 7E NNND boxplot
svglite(paste(panel_output_path, NNND_boxplot_filename, .svg, sep = \), width=4, height=3) boxplot(NNND ~ CMAC.type, subset(STORM.Data, CMAC.type == -Retraction | CMAC.type == ), notch = TRUE, outline = FALSE, main = Neighbour Distance of Nanocluster by CMAC Type, sub = TEST_nnnd_NRvsR$p.value, xlab = type, ylab = Neighbour Distance (nm), plot =
) dev.off()

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rr boxplot(NNND ~ CMAC.type, subset(STORM.Data, CMAC.type == -Retraction | CMAC.type == ), notch = TRUE, outline = FALSE, main = Neighbour Distance of Nanocluster by CMAC Type, sub = TEST_nnnd_NRvsR$p.value, xlab = type, ylab = Neighbour Distance (nm), plot =
)

Boxplots summarising Molecular Localization per nanocluster2

rr STORM.Data\(Molecules <- as.numeric(STORM.Data\)Molecules)

NAs introduced by coercion

rr Molecules.A = subset(STORM.Data\(Molecules, CMAC.type == \Non-Retraction\) Molecules.T = subset(STORM.Data\)Molecules, CMAC.type == ) Mol_Count_boxplot_filename <- 7F Molecule Count boxplot
svglite(paste(panel_output_path, Mol_Count_boxplot_filename, .svg, sep = \), width=4, height=3) TEST_Molecules.A_RetvsFoc <- wilcox.test(Molecules.A, Molecules.T, paired = FALSE) boxplot(Molecules ~ CMAC.type, subset(STORM.Data, CMAC.type == -Retraction | CMAC.type == ), notch = TRUE,outline = FALSE, main = of Integrin avb5 Molecules by CMAC Type, sub = TEST_Molecules.A_RetvsFoc$p.value, xlab = type, ylab = of Integrin avb5 Molecules, plot =
) dev.off()

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rr TEST_Molecules.A_RetvsFoc <- wilcox.test(Molecules.A, Molecules.T, paired = FALSE) boxplot(Molecules ~ CMAC.type, subset(STORM.Data, CMAC.type == -Retraction | CMAC.type == ), notch = TRUE,outline = FALSE, main = of Integrin avb5 Molecules by CMAC Type, sub = TEST_Molecules.A_RetvsFoc$p.value, xlab = type, ylab = of Integrin avb5 Molecules, plot =
)

Figure 8

Import Data for Figure 8

rr # Set your path to input data data_input_path <- /Users/johnlock/Dropbox/A-CMAC Manchester collaboration/NCB submission documents/Resubmission 2/Source_Data
data_file <- Table 1 Statistics Source Data.xlsx
# Import individual data files Residual_Angle_data <- read_xlsx(paste(data_input_path, data_file, sep = /), sheet = 8A_B, col_names = TRUE) Cell_Division_Defect_Quantification <- read_xlsx(paste(data_input_path, data_file, sep = /), sheet = 8C, col_names = TRUE) Cell_Division_Defect_Quantification <- Cell_Division_Defect_Quantification[1:9, 1:4]

Residual Angle Comparisons per Condition

Fig 8A Plot Residual Angle Boxplot of +/- Beeswarm by condition

rr #define panel_file_name boxplot_panel_file_name <- 8A Residual Angle Boxplots
# oldpar <- par() # par(mfrow = c(1,1), mar = c(5,5,1,1)) Residual_Angle_dataCN <- Residual_Angle_data # Set Condition order Residual_Angle_dataCN\(Condition <- factor(Residual_Angle_dataCN\)Condition, c(, 5KD, )) svglite(paste(panel_output_path, boxplot_panel_file_name, .svg, sep = \), width=4, height=6) boxplot(Pre.to.Post.Mitosis.Angle ~ Condition, data = Residual_Angle_dataCN, notch = T, varwidth = T, outline = F, ylab = Angle, col = c(control = #49494A, b5KD = #EC1C24, Rescue = #BCBCBC), ylim = c(0,115)) beeswarm(Pre.to.Post.Mitosis.Angle ~ Condition, data = Residual_Angle_dataCN, method = ‘center’, add = T, col = 2, pch = 1, cex = 0.5) dev.off()

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rr boxplot(Pre.to.Post.Mitosis.Angle ~ Condition, data = Residual_Angle_dataCN, notch = T, varwidth = T, outline = F, ylab = Angle, col = c(control = #49494A, b5KD = #EC1C24, Rescue = #BCBCBC), ylim = c(0,115)) r beeswarm(Pre.to.Post.Mitosis.Angle ~ Condition, data = Residual_Angle_dataCN, method = ‘center’, add = T, col = 2, pch = 1, cex = 0.5)

Fig 8B Plot Residual Angle smoothed density plot per condition

rr #define panel_file_name density_panel_file_name <- 8B Residual Angle Density Plot
svglite(paste(panel_output_path, density_panel_file_name, .svg, sep = \), width=4, height=6) sm.density.compare(Residual_Angle_dataCN\(Pre.to.Post.Mitosis.Angle, Residual_Angle_dataCN\)Condition, col = c(#49494A, #EC1C24, #BCBCBC), lty = c(1,1,1), lwd = 2, h = 5, xlim = c(0,90), xlab = Angle) colfill<-c(#49494A, #EC1C24, #BCBCBC) legend(x=40, y=0.033, levels(Residual_Angle_dataCN$Condition), fill=colfill, bty = ) dev.off()

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rr sm.density.compare(Residual_Angle_dataCN\(Pre.to.Post.Mitosis.Angle, Residual_Angle_dataCN\)Condition, col = c(#49494A, #EC1C24, #BCBCBC), lty = c(1,1,1), lwd = 2, h = 5, xlim = c(0,90), xlab = Angle) r colfill<-c(#49494A, #EC1C24, #BCBCBC) legend(x=40, y=0.033, levels(Residual_Angle_dataCN$Condition), fill=colfill, bty = )

Count cell number per condition

rr Residual_Angle_dataCN %>% group_by(Condition) %>% summarise( n = n())

Mann-Whitney µ test for Residual Angle differences

rr #Note: Equivalent to Wilcoxon rank sum test as data is unpaired b5KD_Control <- subset(Residual_Angle_data, Residual_Angle_data\(Condition == \b5KD\ | Residual_Angle_data\)Condition == ) Utest.b5KD.vs.control <- wilcox.test(Pre.to.Post.Mitosis.Angle ~ Condition, data = b5KD_Control) Utest.b5KD.vs.control.pval <- Utest.b5KD.vs.control\(p.value b5KD_Rescue <- subset(Residual_Angle_data, Residual_Angle_data\)Condition == 5KD | Residual_Angle_data\(Condition == \Rescue\) Utest.b5KD.vs.Rescue <- wilcox.test(Pre.to.Post.Mitosis.Angle ~ Condition, data = b5KD_Rescue) Utest.b5KD.vs.Rescue.pval <- Utest.b5KD.vs.Rescue\)p.value Control_Rescue <- subset(Residual_Angle_data, Residual_Angle_data\(Condition == \Control\ | Residual_Angle_data\)Condition == ) Utest.control.vs.Rescue <- wilcox.test(Pre.to.Post.Mitosis.Angle ~ Condition, data = Control_Rescue) Utest.control.vs.Rescue.pval <- Utest.control.vs.Rescue\(p.value Mann_Whitney_Scores <- data.frame(Condition_Comparison = c(\b5KD_vs_Control\, \b5KD_vs_Rescue\, \Control_vs_Rescue\)) Mann_Whitney_Scores\)pvalue <- rbind(Utest.b5KD.vs.control.pval, Utest.b5KD.vs.Rescue.pval, Utest.control.vs.Rescue.pval) print(Mann_Whitney_Scores)

Residual Angle Comparisons per Condition per Experiment

Plot Residual Angle Boxplot by condition by experiment

rr Residual_Angle_dataFN <- Residual_Angle_data Residual_Angle_dataFN\(File.Name <- factor(Residual_Angle_dataFN\)File.Name, c(_3_MMStack_control Hela 01.ome.tif, _3_MMStack_control Hela 02.ome.tif, _3_MMStack_b5 KD plus GFP 01.ome.tif, _3_MMStack_b5 KD plus GFP 02.ome.tif, _3_MMStack_b5 KD plus WTb5-GFP 01.ome.tif, _3_MMStack_b5 KD plus WTb5-GFP 02.ome.tif)) boxplot(Pre.to.Post.Mitosis.Angle ~ File.Name, data = Residual_Angle_dataFN, notch = T, varwidth = T, outline = F, ylab = Angle, col = c(#49494A, #49494A, #EC1C24, #EC1C24,#BCBCBC, #BCBCBC), ylim = c(0,115), names = c(_01, _02, 5KD_01, 5KD_02, _01, _02))

rr # beeswarm(Pre.to.Post.Mitosis.Angle ~ File.Name, data = Residual_Angle_dataFN, method = ‘swarm’, add = T, col = 2, pch = 0, cex = 0.7)

Plot Plot Residual Angle smoothed density plot per condition per experiment

rr sm.density.compare(Residual_Angle_dataFN\(Pre.to.Post.Mitosis.Angle, Residual_Angle_dataFN\)File.Name, col = c(#49494A, #49494A, #EC1C24, #EC1C24,#BCBCBC,#BCBCBC), h=5, lty = c(1,5,1,5,1,5), lwd = 2, xlim = c(0,90), xlab = Angle) legend(x=23, y= 0.05, levels(Residual_Angle_dataFN$File.Name), fill=c(#49494A, #49494A, #EC1C24, #EC1C24,#BCBCBC,#BCBCBC), bty = ‘n’)

Count cell number per condition per experiment

rr Residual_Angle_dataFN %>% group_by(File.Name) %>% summarise( n = n())

Cell Division Defect Quantification

Fig 8C Cell Division Defect Rate Plotting

rr CDD_boxplot_panel_file_name <- 8C Cell Division Defect Rate Boxplot
Cell_Division_Defect_Quantification\(Condition <- factor(Cell_Division_Defect_Quantification\)Condition, c(, 5KD, )) svglite(paste(panel_output_path, CDD_boxplot_panel_file_name, .svg, sep = \), width=4, height=6) boxplot(normal.cell.division ~ Condition, data = Cell_Division_Defect_Quantification, outline = FALSE, boxlty = 0, whisklty = 0, staplelty = 0, ylab = Division Percentage, ylim = c(0,115), medlwd = 0.0) beeswarm(normal.cell.division ~ Condition, data = Cell_Division_Defect_Quantification, method = ‘center’, add = T, col = c(control = #49494A, b5KD = #EC1C24, Rescue = #BCBCBC), ylim = c(0,115), pch = 19, cex = 1.3) dev.off()

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rr boxplot(normal.cell.division ~ Condition, data = Cell_Division_Defect_Quantification, outline = FALSE, boxlty = 0, whisklty = 0, staplelty = 0, ylab = Division Percentage, ylim = c(0,115), medlwd = 0.0) r beeswarm(normal.cell.division ~ Condition, data = Cell_Division_Defect_Quantification, method = ‘center’, add = T, col = c(control = #49494A, b5KD = #EC1C24, Rescue = #BCBCBC), ylim = c(0,115), pch = 19, cex = 1.3)

Cell Division Defect Summaries

rr Division_Defect_Summary <- Cell_Division_Defect_Quantification %>% group_by(Condition) %>% summarise( Normal_Division = mean(normal.cell.division), Abnormal_Division = mean(abnormal.cell.division) )

print(Division_Defect_Summary)

T-testing for Cell Division Defect Rate differences

rr b5KD_Control_CDD <- subset(Cell_Division_Defect_Quantification, Condition == 5KD | Condition == ) T_test_b5KD_Control_CDD <- t.test(normal.cell.division ~ Condition, data = b5KD_Control_CDD)\(p.value b5KD_Rescue_CDD <- subset(Cell_Division_Defect_Quantification, Condition == \b5KD\ | Condition == \Rescue\) T_test_b5KD_Rescue_CDD <- t.test(normal.cell.division ~ Condition, data = b5KD_Rescue_CDD)\)p.value Control_Rescue_CDD <- subset(Cell_Division_Defect_Quantification, Condition ==  | Condition == ) T_test_Control_Rescue_CDD <- t.test(normal.cell.division ~ Condition, data = Control_Rescue_CDD)\(p.value Ttest_Scores_CDD <- data.frame(Condition_Comparison = c(\b5KD_Control_CDD\, \b5KD_Rescue_CDD\, \Control_Rescue_CDD\)) Ttest_Scores_CDD\)pvalue <- rbind(T_test_b5KD_Control_CDD, T_test_b5KD_Rescue_CDD, T_test_Control_Rescue_CDD) print(Ttest_Scores_CDD)

Supp Fig 5

Import Data Supp Fig 5

rr # Set your path to input data data_input_path <- /Users/johnlock/Dropbox/A-CMAC Manchester collaboration/NCB submission documents/Resubmission 2/Source_Data
data_file <- Table 1 Statistics Source Data.xlsx
# Import individual data files Arp23_Inhibition_Data <- read_xlsx(paste(data_input_path, data_file, sep = /), sheet = 5C, col_names = TRUE) Arp23_Inhibition_Data\(Drug <- recode(Arp23_Inhibition_Data\)Drug, Arp_Inhib_CTRL_689 = _CTRL_689)

Analyse Arp2/3 inhibitor responses relative to control

Boxplot changes in reticular adhesion b5 intensity2

rr Arp23_b5_intensity_boxplots_filename <- SF5C Arp23_b5_intensity_boxplots
cell.numbers.ARP23.screen <- count(Arp23_Inhibition_Data, Drug) count(Arp23_Inhibition_Data, Drug) r mean.cell.numbers.ARP23.screen <- summarise(cell.numbers.ARP23.screen, avg = mean(n), SD = sd(n), sum(n)) summarise(cell.numbers.ARP23.screen, avg = mean(n), SD = sd(n), sum(n))

rr svglite(paste(panel_output_path, Arp23_b5_intensity_boxplots_filename, .svg, sep = \), width=7, height=5) p1 <- ggplot(Arp23_Inhibition_Data, aes(x=Drug, y=Median.Mean..z.score, color = factor(Drug))) + geom_boxplot(notch = TRUE, outlier.colour = ) + scale_x_discrete(limits=c(_CTRL_689, _Inhib_666)) + theme(axis.text.x = element_text(angle=45, hjust = 1)) + ylim(-3, 6) + ylab(Reticular Adhesion Intensity) + theme(legend.position=) ## plot T-CMAC intensity Z-score ggplot2 boxplot - per Drug p2 <- ggplot(Arp23_Inhibition_Data, aes(x=Drug, y=Median.Mean._T.CMACs.z.score, color = factor(Drug))) + geom_boxplot(notch = TRUE, outlier.colour = ) + scale_x_discrete(limits=c(_CTRL_689, _Inhib_666)) + theme(axis.text.x = element_text(angle=45, hjust = 1)) + ylim(-3, 6) + ylab(Focal Adhesion Intensity) + theme(legend.position=) ## plot intensity ratio Z-score ggplot2 boxplot - per Drug p3 <- ggplot(Arp23_Inhibition_Data, aes(x=Drug, y=Ratio_A.CMAC.to.T.CMAC_mean_intensity.z.score, color = factor(Drug))) + geom_boxplot(notch = TRUE, outlier.colour = ) + scale_x_discrete(limits=c(_CTRL_689, _Inhib_666)) + theme(axis.text.x = element_text(angle=45, hjust = 1)) + ylim(-3, 6) + ylab(Reticular:Focal Adhesion Intensity) + theme(legend.position=) grid.arrange(p1, p2, p3, ncol = 3) dev.off()

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rr p1 <- ggplot(Arp23_Inhibition_Data, aes(x=Drug, y=Median.Mean..z.score, color = factor(Drug))) + geom_boxplot(notch = TRUE, outlier.colour = ) + scale_x_discrete(limits=c(_CTRL_689, _Inhib_666)) + theme(axis.text.x = element_text(angle=45, hjust = 1)) + ylim(-3, 6) + ylab(Reticular Adhesion Intensity) + theme(legend.position=) ## plot T-CMAC intensity Z-score ggplot2 boxplot - per Drug p2 <- ggplot(Arp23_Inhibition_Data, aes(x=Drug, y=Median.Mean._T.CMACs.z.score, color = factor(Drug))) + geom_boxplot(notch = TRUE, outlier.colour = ) + scale_x_discrete(limits=c(_CTRL_689, _Inhib_666)) + theme(axis.text.x = element_text(angle=45, hjust = 1)) + ylim(-3, 6) + ylab(Focal Adhesion Intensity) + theme(legend.position=) ## plot intensity ratio Z-score ggplot2 boxplot - per Drug p3 <- ggplot(Arp23_Inhibition_Data, aes(x=Drug, y=Ratio_A.CMAC.to.T.CMAC_mean_intensity.z.score, color = factor(Drug))) + geom_boxplot(notch = TRUE, outlier.colour = ) + scale_x_discrete(limits=c(_CTRL_689, _Inhib_666)) + theme(axis.text.x = element_text(angle=45, hjust = 1)) + ylim(-3, 6) + ylab(Reticular:Focal Adhesion Intensity) + theme(legend.position=) grid.arrange(p1, p2, p3, ncol = 3)

Mann-Whitney test for significant Drug treatment effects on Reticular adhesion intensity2

rr p_values <- c() for (i in 1:length(unique(Arp23_Inhibition_Data\(Drug))){ for (j in 1:length(unique(Arp23_Inhibition_Data\)Drug))){ Drug_1 = subset(Arp23_Inhibition_Data, Arp23_Inhibition_Data\(Drug == unique(Arp23_Inhibition_Data\)Drug)[i]) Drug_1\(Drug_Number = as.factor(i) Drug_2 = subset(Arp23_Inhibition_Data, Arp23_Inhibition_Data\)Drug == unique(Arp23_Inhibition_Data\(Drug)[j]) Drug_2\)Drug_Number = as.factor(j+100) Drug_Pair = rbind(Drug_1, Drug_2) Drug_Pair_ttest = wilcox.test(Median.Mean..z.score ~ Drug_Number, data = Drug_Pair)\(p.value p_values = rbind(p_values, Drug_Pair_ttest) } } p_values <- as.vector(p_values) p_value_matrix <- matrix(p_values,nrow = length(unique(Arp23_Inhibition_Data\)Drug)),ncol = length(unique(Arp23_Inhibition_Data\(Drug))) columns <- as.vector(unique(Arp23_Inhibition_Data\)Drug)) rows <- as.vector(unique(Arp23_Inhibition_Data\(Drug)) p_value_dataframe <- as.data.frame(p_value_matrix, row.names = rows) names(p_value_dataframe) <- columns # print(p_value_dataframe) # Introduce Holm (also called Holm-Bonferroni) p-value correction (used because it gives same stringency against false positives (type 1 errors) with lower probability of false negatives (type 2 errors))) corrected_p_values <- p.adjust(p_values, method = \holm\, n = length(p_values)) corrected_p_value_matrix6 <- matrix(corrected_p_values,nrow = length(unique(Arp23_Inhibition_Data\)Drug)),ncol = length(unique(Arp23_Inhibition_Data\(Drug))) columns <- as.vector(unique(Arp23_Inhibition_Data\)Drug)) rows <- as.vector(unique(Arp23_Inhibition_Data$Drug)) corrected_p_value_dataframe <- as.data.frame(corrected_p_value_matrix6, row.names = rows) names(corrected_p_value_dataframe) <- columns print(corrected_p_value_dataframe)

Mann-Whitney test for significant Drug treatment effects on Focal adhesion intensity2

rr p_values <- c() for (i in 1:length(unique(Arp23_Inhibition_Data\(Drug))){ for (j in 1:length(unique(Arp23_Inhibition_Data\)Drug))){ Drug_1 = subset(Arp23_Inhibition_Data, Arp23_Inhibition_Data\(Drug == unique(Arp23_Inhibition_Data\)Drug)[i]) Drug_1\(Drug_Number = as.factor(i) Drug_2 = subset(Arp23_Inhibition_Data, Arp23_Inhibition_Data\)Drug == unique(Arp23_Inhibition_Data\(Drug)[j]) Drug_2\)Drug_Number = as.factor(j+100) Drug_Pair = rbind(Drug_1, Drug_2) Drug_Pair_ttest = wilcox.test(Median.Mean._T.CMACs.z.score ~ Drug_Number, data = Drug_Pair)\(p.value p_values = rbind(p_values, Drug_Pair_ttest) } } p_values <- as.vector(p_values) p_value_matrix <- matrix(p_values,nrow = length(unique(Arp23_Inhibition_Data\)Drug)),ncol = length(unique(Arp23_Inhibition_Data\(Drug))) columns <- as.vector(unique(Arp23_Inhibition_Data\)Drug)) rows <- as.vector(unique(Arp23_Inhibition_Data\(Drug)) p_value_dataframe <- as.data.frame(p_value_matrix, row.names = rows) names(p_value_dataframe) <- columns # print(p_value_dataframe) # Introduce Holm (also called Holm-Bonferroni) p-value correction (used because it gives same stringency against false positives (type 1 errors) with lower probability of false negatives (type 2 errors))) corrected_p_values <- p.adjust(p_values, method = \holm\, n = length(p_values)) corrected_p_value_matrix7 <- matrix(corrected_p_values,nrow = length(unique(Arp23_Inhibition_Data\)Drug)),ncol = length(unique(Arp23_Inhibition_Data\(Drug))) columns <- as.vector(unique(Arp23_Inhibition_Data\)Drug)) rows <- as.vector(unique(Arp23_Inhibition_Data$Drug)) corrected_p_value_dataframe <- as.data.frame(corrected_p_value_matrix7, row.names = rows) names(corrected_p_value_dataframe) <- columns print(corrected_p_value_dataframe)

Mann-Whitney test for significant Drug treatment effects on intensity ratio Z-score2

rr p_values <- c() for (i in 1:length(unique(Arp23_Inhibition_Data\(Drug))){ for (j in 1:length(unique(Arp23_Inhibition_Data\)Drug))){ Drug_1 = subset(Arp23_Inhibition_Data, Arp23_Inhibition_Data\(Drug == unique(Arp23_Inhibition_Data\)Drug)[i]) Drug_1\(Drug_Number = as.factor(i) Drug_2 = subset(Arp23_Inhibition_Data, Arp23_Inhibition_Data\)Drug == unique(Arp23_Inhibition_Data\(Drug)[j]) Drug_2\)Drug_Number = as.factor(j+100) Drug_Pair = rbind(Drug_1, Drug_2) Drug_Pair_ttest = wilcox.test(Ratio_A.CMAC.to.T.CMAC_mean_intensity.z.score ~ Drug_Number, data = Drug_Pair)\(p.value p_values = rbind(p_values, Drug_Pair_ttest) } } p_values <- as.vector(p_values) p_value_matrix <- matrix(p_values,nrow = length(unique(Arp23_Inhibition_Data\)Drug)),ncol = length(unique(Arp23_Inhibition_Data\(Drug))) columns <- as.vector(unique(Arp23_Inhibition_Data\)Drug)) rows <- as.vector(unique(Arp23_Inhibition_Data\(Drug)) p_value_dataframe <- as.data.frame(p_value_matrix, row.names = rows) names(p_value_dataframe) <- columns # print(p_value_dataframe) # Introduce Holm (also called Holm-Bonferroni) p-value correction (used because it gives same stringency against false positives (type 1 errors) with lower probability of false negatives (type 2 errors))) corrected_p_values <- p.adjust(p_values, method = \holm\, n = length(p_values)) corrected_p_value_matrix8 <- matrix(corrected_p_values,nrow = length(unique(Arp23_Inhibition_Data\)Drug)),ncol = length(unique(Arp23_Inhibition_Data\(Drug))) columns <- as.vector(unique(Arp23_Inhibition_Data\)Drug)) rows <- as.vector(unique(Arp23_Inhibition_Data$Drug)) corrected_p_value_dataframe <- as.data.frame(corrected_p_value_matrix8, row.names = rows) names(corrected_p_value_dataframe) <- columns print(corrected_p_value_dataframe)

Supp Fig 6

Import Data Supp Fig 6

rr # Set your path to input data data_input_path <- /Users/johnlock/Dropbox/A-CMAC Manchester collaboration/NCB submission documents/Resubmission 2/Source_Data
data_file <- Table 1 Statistics Source Data.xlsx
# Import individual data files Secondary.siRNA.Screen.Data <- read_xlsx(paste(data_input_path, data_file, sep = /), sheet = 6A, col_names = TRUE)

Analyse Secondary siRNA Screen responses

Boxplot changes in reticular to focal adhesion b5 intensity ratio

rr Secondary_siRNA_Screen_boxplots_filename <- SF6A Secondary_siRNA_Screen_boxplot_summary
Secondary.siRNA.Screen.Data\(siRNA_Target <- recode(Secondary.siRNA.Screen.Data\)siRNA_Target, PIK3C2a_1 = 3C2A_1) Secondary.siRNA.Screen.Data\(siRNA_Target <- recode(Secondary.siRNA.Screen.Data\)siRNA_Target, PIK3C2a_2 = 3C2A_2) Secondary.siRNA.Screen.Data\(siRNA_Target <- recode(Secondary.siRNA.Screen.Data\)siRNA_Target, PIK3C2a_3 = 3C2A_3) Secondary.siRNA.Screen.Data\(siRNA_Target <- recode(Secondary.siRNA.Screen.Data\)siRNA_Target, PIK3C2a_4 = 3C2A_4) cell.numbers.secondary.screen <- count(Secondary.siRNA.Screen.Data, siRNA_Target) count(Secondary.siRNA.Screen.Data, siRNA_Target) r mean.cell.numbers.secondary.screen <- summarise(cell.numbers.secondary.screen, avg = mean(n), SD = sd(n), sum(n)) summarise(cell.numbers.secondary.screen, avg = mean(n), SD = sd(n), sum(n))

rr svglite(paste(panel_output_path, Secondary_siRNA_Screen_boxplots_filename, .svg, sep = \), width=7, height=5) ggplot(Secondary.siRNA.Screen.Data, aes(x=siRNA_Target, y=Ratio_A.CMAC.to.T.CMAC_mean_intensity.z.score, color = factor(siRNA_Treatment_Type))) + geom_boxplot(notch = TRUE, outlier.colour = ) + theme(axis.text.x = element_text(angle=90)) + ylim(-3, 6) + scale_x_discrete(limits=c(,,_3Ctrl_Pool, -TARGETplus Non-targeting Control, Non-targeting Control, , , 5, _1, _2, _3, _4, 4K2A_1, 4K2A_2, 4K2A_3, 4K2A_4, 4KA_1, 4KA_2, 4KA_3, 4KA_4, 5K1B_1, 5K1B_2, 5K1B_3, 5K1B_4, 5K1C_1, 5K1C_2, 5K1C_3, 5K1C_4, _1, _2, _3, _4, 3C2A_1, 3C2A_2, 3C2A_3, 3C2A_4)) dev.off()

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rr ggplot(Secondary.siRNA.Screen.Data, aes(x=siRNA_Target, y=Ratio_A.CMAC.to.T.CMAC_mean_intensity.z.score, color = factor(siRNA_Treatment_Type))) + geom_boxplot(notch = TRUE, outlier.colour = ) + theme(axis.text.x = element_text(angle=90)) + ylim(-3, 6) + scale_x_discrete(limits=c(,,_3Ctrl_Pool, -TARGETplus Non-targeting Control, Non-targeting Control, , , 5, _1, _2, _3, _4, 4K2A_1, 4K2A_2, 4K2A_3, 4K2A_4, 4KA_1, 4KA_2, 4KA_3, 4KA_4, 5K1B_1, 5K1B_2, 5K1B_3, 5K1B_4, 5K1C_1, 5K1C_2, 5K1C_3, 5K1C_4, _1, _2, _3, _4, 3C2A_1, 3C2A_2, 3C2A_3, 3C2A_4))

Mann-Whitney test for significant siRNA treatment effects2

rr siRNA.conditions.for.U.test <- subset(Secondary.siRNA.Screen.Data, siRNA_Target !=  & siRNA_Target !=  & siRNA_Target != _3Ctrl_Pool & siRNA_Target != Non-targeting Control) p_values <- c() for (i in 1:length(unique(siRNA.conditions.for.U.test\(siRNA_Target))){ for (j in 1:length(unique(siRNA.conditions.for.U.test\)siRNA_Target))){ siRNA_Target_1 = subset(siRNA.conditions.for.U.test, siRNA.conditions.for.U.test\(siRNA_Target == unique(siRNA.conditions.for.U.test\)siRNA_Target)[i]) siRNA_Target_1\(siRNA_Target_Number = as.factor(i) siRNA_Target_2 = subset(siRNA.conditions.for.U.test, siRNA.conditions.for.U.test\)siRNA_Target == unique(siRNA.conditions.for.U.test\(siRNA_Target)[j]) siRNA_Target_2\)siRNA_Target_Number = as.factor(j+100) siRNA_Target_Pair = rbind(siRNA_Target_1, siRNA_Target_2) siRNA_Target_Pair_ttest = wilcox.test(Ratio_A.CMAC.to.T.CMAC_mean_intensity.z.score ~ siRNA_Target_Number, data = siRNA_Target_Pair)\(p.value p_values = rbind(p_values, siRNA_Target_Pair_ttest) } } p_values <- as.vector(p_values) p_value_matrix <- matrix(p_values,nrow = length(unique(siRNA.conditions.for.U.test\)siRNA_Target)),ncol = length(unique(siRNA.conditions.for.U.test\(siRNA_Target))) columns <- as.vector(unique(siRNA.conditions.for.U.test\)siRNA_Target)) rows <- as.vector(unique(siRNA.conditions.for.U.test\(siRNA_Target)) p_value_dataframe <- as.data.frame(p_value_matrix, row.names = rows) names(p_value_dataframe) <- columns # print(p_value_dataframe) # Introduce Holm (also called Holm-Bonferroni) p-value correction (used because it gives same stringency against false positives (type 1 errors) with lower probability of false negatives (type 2 errors))) corrected_p_values <- p.adjust(p_values, method = \holm\, n = length(p_values)) corrected_p_value_matrix9 <- matrix(corrected_p_values,nrow = length(unique(siRNA.conditions.for.U.test\)siRNA_Target)),ncol = length(unique(siRNA.conditions.for.U.test\(siRNA_Target))) columns <- as.vector(unique(siRNA.conditions.for.U.test\)siRNA_Target)) rows <- as.vector(unique(siRNA.conditions.for.U.test$siRNA_Target)) corrected_p_value_dataframe <- as.data.frame(corrected_p_value_matrix9, row.names = rows) names(corrected_p_value_dataframe) <- columns print(corrected_p_value_dataframe)

Plot heatmap of corrected p_values2

rr heatmap.2(log(corrected_p_value_matrix9), dendrogram = ‘none’, Rowv = FALSE, Colv = FALSE, labRow = rows, labCol = columns, srtCol = 45, cexRow = 1, cexCol = 1, margins = c(7, 9), symm = TRUE, revC = FALSE, breaks = c(log(1e-300), log(1e-50), log(1e-10), log(1e-6), log(0.001), log(0.01), log(0.05), log(1)), rowsep = 1:nrow(corrected_p_value_matrix9), colsep = 1:ncol(corrected_p_value_matrix9), sepcolor = ‘darkgrey’, sepwidth = c(0.02,0.02), trace = ‘none’, col = c(, , , , , , ), denscol = NULL, keysize = 1.5, key.title = NA)

---
title: "Statistical Analsyses Code Lock et al NCB 2018"
author: "Lock et al"
date: "`r Sys.Date()`"
output:
  html_notebook:
    theme: cosmo
    toc: yes
    toc_depth: 4
  html_document:
    number_sections: yes
    theme: cosmo
    toc: yes
    toc_depth: 4
  pdf_document:
    number_sections: yes
    toc: yes
  word_document:
    toc: yes
always_allow_html: yes
---

**

**This R markdown document (.Rmd) contains analysis, visualisation and statistical testing relating to research paper "Reticular adhesions mediate cell-matrix attachment during mitosis” by Lock et al (NCB, 2018).** 

**This code uses the R markdown framework and enables production of statistical and graphical outputs equivalent to those presented in the paper. Some purely aesthetic differences exist to final paper figures given subsequent editing (adobe illustrator) for figure embedding. All R packages necessary to run this code will be installed automatically upon running this code.**

**This code calls the source data file named "Supplementary Table 1 Statistics Source Data.xlsx" and selects individual data sheets pertinent to each analysis. These sheets are named according the Figure Panel that they underpin. The user must place this source data file in a location of their choice and modify the "data_input_path" variable to reflect this location. Graphical outputs are saved according to the relevant Figure Panel names (with some additional descriptive information) to a location defined by the variable "panel_output_path". By default this is in a folder named "Graphical_Outputs" within the defined "data_input_path". Note that some figure panels were generated directly within Excel and these can be found within the source data file.**

**In addition to specific .svg graphical outputs, this R markdown code will produce an HTML notebook that is useful to collectively preview and efficiently share outputs and their underlying code. This output HTML notebook file matches the name of this source .Rmd file, i.e. "Statistical_Analysis_Code_Lock_et_al_NCB_2018.nb.html".** 

**


# Setup
```{r libraries and Environment Clean, message=FALSE, warning=FALSE, include=FALSE}
# Install and apply required packages

usePackage <- function(p) 
{
  if (!is.element(p, installed.packages()[,1]))
    install.packages(p, dep = TRUE)
  require(p, character.only = TRUE)
}


usePackage('beeswarm')
usePackage('sm')
usePackage('dplyr')
usePackage('tidyverse')
usePackage('readxl')
usePackage('knitr')
usePackage('svglite')
usePackage('gplots')
usePackage('ggplot2')
usePackage('Hmisc')
usePackage('zoo')
usePackage('tidyquant')
usePackage('spatstat')
usePackage('gridExtra')
usePackage('grid')
usePackage('GGally') 

# Clear all pre-existing objects from the current workspace
rm(list = ls())

```

## Input Path

```{r Input Path}
# Set your path to input data

data_input_path <- ".../Source_Data"

# name of the source data file which you have placed in the above location
data_file <- "Supplementary Table 1 Statistics Source Data.xlsx"

```


## Output Path
```{r Output Path}
# Define panel output path

panel_output_path <- paste(data_input_path, "Graphical_Outputs/", sep = "/")

```

# Figure 1

## Import Data for Figure 1
```{r mport Data for Figure 1}

# Import individual data files
Mass.Spec.Data <- read_xlsx(paste(data_input_path, data_file, sep = "/"), sheet = "Fig1A", col_names = TRUE)

b5.int.vs.VN.conc <- read_xlsx(paste(data_input_path, data_file, sep = "/"), sheet = "Fig1G", col_names = TRUE)


```

## Mass Spec of Integrin subunits in long-term culture
### Figure 1A boxplot of Integrin subunits spectral counts
```{r Figure 1A boxplot of Integrin subunits spectral counts}

#define panel_file_name
Integrin_Spectral_Counts_File_Name <- "Panel 1A Integrin Spectral Counts Boxplot"

boxplot(Mean.Spectral.Counts ~ Integrin.Subunit, data = Mass.Spec.Data, notch = FALSE, varwidth = FALSE, las=1, ylab = "Mean Spectral Counts", col = "grey", ylim = c(0, 50), outline = FALSE, boxlty = 0, whisklty = 0, staplelty = 0, medlwd = 0.5, boxfill = NA)
beeswarm(Mean.Spectral.Counts ~ Integrin.Subunit, data = Mass.Spec.Data, method = 'center', add = T, col = "red", pch = 19, cex = 0.7)

svglite(paste(panel_output_path, Integrin_Spectral_Counts_File_Name, ".svg", sep = ""), width=8, height=6)

boxplot(Mean.Spectral.Counts ~ Integrin.Subunit, data = Mass.Spec.Data, notch = FALSE, varwidth = FALSE, las=1, ylab = "Mean Spectral Counts", col = "grey", ylim = c(0, 50), outline = FALSE, boxlty = 0, whisklty = 0, staplelty = 0, medlwd = 0.5, boxfill = NA)
beeswarm(Mean.Spectral.Counts ~ Integrin.Subunit, data = Mass.Spec.Data, method = 'center', add = T, col = "red", pch = 19, cex = 0.7)

dev.off()

```
#### T-testing for spectral count differences differences
```{r}
AVvsB1 <- subset(Mass.Spec.Data, Integrin.Subunit == "ITGAV" | Integrin.Subunit == "ITGB1")
T_test_AVvsB1 <- t.test(Mean.Spectral.Counts ~ Integrin.Subunit, data = AVvsB1, alternative = "two.sided", paired = FALSE)$p.value

T_test_AVvsB1

B5vsB1 <- subset(Mass.Spec.Data, Integrin.Subunit == "ITGB5" | Integrin.Subunit == "ITGB1")
T_test_B5vsB1 <- t.test(Mean.Spectral.Counts ~ Integrin.Subunit, data = B5vsB1, alternative = "two.sided", paired = FALSE)$p.value

T_test_B5vsB1

```


## Integrin intensity responses to Vitronection concentration in talin-positive and talin-negative adhesions 

### Boxplot of Integrin intensity vs Vitronection concentration in talin-positive and talin-negative adhesions
```{r Boxplot of Integrin intensity vs Vitronection concentration in talin-positive and talin-negative adhesions}
#define panel_file_name
b5_Intensity_File_Name <- "Panel 1G Integrin b5 Intensity"

names(b5.int.vs.VN.conc) <- c("Talin_Pos_vs_Neg", "Condition", "Mean.integrin.b5.Intensity")
b5.int.vs.VN.conc$Talin_Pos_vs_Neg <- recode(b5.int.vs.VN.conc$Talin_Pos_vs_Neg, Atypical = "Talin_Neg")
b5.int.vs.VN.conc$Talin_Pos_vs_Neg <- recode(b5.int.vs.VN.conc$Talin_Pos_vs_Neg, typical = "Talin_Pos")


b5.int.vs.VN.conc$Group <- paste(b5.int.vs.VN.conc$Talin_Pos_vs_Neg, b5.int.vs.VN.conc$Condition, sep = "_")
b5.int.vs.VN.conc$Group <- factor(b5.int.vs.VN.conc$Group, levels = c("Talin_Pos_1VN", "Talin_Neg_1VN", "Talin_Pos_3VN", "Talin_Neg_3VN", "Talin_Pos_10VN", "Talin_Neg_10VN"))


ggplot(data = b5.int.vs.VN.conc, aes(x = Group, y = Mean.integrin.b5.Intensity)) + geom_boxplot(aes(fill = Talin_Pos_vs_Neg, ymin=..lower.., ymax=..upper..), outlier.shape=NA, notch = TRUE) + scale_fill_manual(values = c("Talin_Pos" = "#4F80BC", "Talin_Neg" = "#C0504F")) + theme_classic() + coord_cartesian(ylim = c(500, 1300)) + theme(axis.text.x = element_text(angle = 45, hjust = 1))

svglite(paste(panel_output_path, b5_Intensity_File_Name, ".svg", sep = ""), width=5, height=8)

ggplot(data = b5.int.vs.VN.conc, aes(x = Group, y = Mean.integrin.b5.Intensity)) + geom_boxplot(aes(fill = Talin_Pos_vs_Neg, ymin=..lower.., ymax=..upper..), outlier.shape=NA, notch = TRUE) + scale_fill_manual(values = c("Talin_Pos" = "#4F80BC", "Talin_Neg" = "#C0504F")) + theme_classic() + coord_cartesian(ylim = c(500, 1300)) + theme(axis.text.x = element_text(angle = 45, hjust = 1))

dev.off()

```

#### Wilcoxon Rank Sum testing of b5 intensity changes given increased Vitronectin
```{r Wilcoxon Rank Sum testing of b5 intensity changes given increased Vitronectin}
TalinPos = subset(b5.int.vs.VN.conc, b5.int.vs.VN.conc$Talin_Pos_vs_Neg == "Talin_Pos")

TalinPos_VN1_vs_VN3 <- subset(b5.int.vs.VN.conc, b5.int.vs.VN.conc$Talin_Pos_vs_Neg == "Talin_Pos" & b5.int.vs.VN.conc$Condition == "1VN" | b5.int.vs.VN.conc$Talin_Pos_vs_Neg == "Talin_Pos" & b5.int.vs.VN.conc$Condition == "3VN" )
TalinPos_VN3_vs_VN10 <- subset(b5.int.vs.VN.conc, b5.int.vs.VN.conc$Talin_Pos_vs_Neg == "Talin_Pos" & b5.int.vs.VN.conc$Condition == "3VN" | b5.int.vs.VN.conc$Talin_Pos_vs_Neg == "Talin_Pos" & b5.int.vs.VN.conc$Condition == "10VN" )

Mu_Test_TalinPos_VN1_vs_VN3 = wilcox.test(Mean.integrin.b5.Intensity ~ Condition, data = TalinPos_VN1_vs_VN3)$p.value
Mu_Test_TalinPos_VN3_vs_VN10 = wilcox.test(Mean.integrin.b5.Intensity ~ Condition, data = TalinPos_VN3_vs_VN10)$p.value

TalinNeg_VN1_vs_VN3 <- subset(b5.int.vs.VN.conc, b5.int.vs.VN.conc$Talin_Pos_vs_Neg == "Talin_Neg" & b5.int.vs.VN.conc$Condition == "1VN" | b5.int.vs.VN.conc$Talin_Pos_vs_Neg == "Talin_Neg" & b5.int.vs.VN.conc$Condition == "3VN" )
TalinNeg_VN3_vs_VN10 <- subset(b5.int.vs.VN.conc, b5.int.vs.VN.conc$Talin_Pos_vs_Neg == "Talin_Neg" & b5.int.vs.VN.conc$Condition == "3VN" | b5.int.vs.VN.conc$Talin_Pos_vs_Neg == "Talin_Neg" & b5.int.vs.VN.conc$Condition == "10VN" )

Mu_Test_TalinNeg_VN1_vs_VN3 = wilcox.test(Mean.integrin.b5.Intensity ~ Condition, data = TalinNeg_VN1_vs_VN3)$p.value
Mu_Test_TalinNeg_VN3_vs_VN10 = wilcox.test(Mean.integrin.b5.Intensity ~ Condition, data = TalinNeg_VN3_vs_VN10)$p.value

```

# Figure 2

## Import Data for Figure 2
```{r mport Data for Figure 2}

# Set your path to input data
data_input_path <- "/Users/johnlock/Dropbox/A-CMAC Manchester collaboration/NCB submission documents/Resubmission 2/Source_Data"
data_file <- "Supplementary Table 1 Statistics Source Data.xlsx"

# Import individual data files
FRAP.data <- read_xlsx(paste(data_input_path, data_file, sep = "/"), sheet = "Fig2S_T", col_names = TRUE)

STORM.Data <- read_xlsx(paste(data_input_path, data_file, sep = "/"), sheet = "Fig2V_W", col_names = TRUE)

```

## Analysis of integrin b5 FRAP recovery curves in Reticular and Focal adhesions

### Integrin b5 FRAP analysis comparing Reticular and Focal adhesions
```{r Integrin b5 FRAP analysis comparing Reticular and Focal adhesions}
#define panel_file_name
FRAP_Curve_File_Name <- "Panel 2S b5 FRAP recovery"

names(FRAP.data) <- c("Standardised_b5_FRAP_recovery_relative_to_prebleach", "Time", "Bleach_ROI", "Reticular_vs_Focal")
FRAP.data$Reticular_vs_Focal <- recode(FRAP.data$Reticular_vs_Focal, atypical = "Reticular")
FRAP.data$Reticular_vs_Focal <- recode(FRAP.data$Reticular_vs_Focal, typical = "Focal")


ggplot(data=FRAP.data, aes(x=Time, y=Standardised_b5_FRAP_recovery_relative_to_prebleach, colour = Reticular_vs_Focal), alpha = 0.1) +
  stat_summary(fun.data ="mean_cl_normal", alpha = 0.5) + theme_classic() + scale_colour_manual(values = c("Reticular" = "#BF4C49", "Focal" = "#4B7FBB")) + geom_smooth(method = "loess", span = 0.2, size = 1, fill = "#3A3A3A")


svglite(paste(panel_output_path, FRAP_Curve_File_Name, ".svg", sep = ""), width=6, height=4)

ggplot(data=FRAP.data, aes(x=Time, y=Standardised_b5_FRAP_recovery_relative_to_prebleach, colour = Reticular_vs_Focal), alpha = 0.1) +
  stat_summary(fun.data ="mean_cl_normal", alpha = 0.5) + theme_classic() + scale_colour_manual(values = c("Reticular" = "#BF4C49", "Focal" = "#4B7FBB")) + geom_smooth(method = "loess", span = 0.2, size = 1, fill = "#3A3A3A")

dev.off()

```

### KS test of FRAP recovery curves
```{r KS test of FRAP recovery curves}
Reticular.FRAP.data <- subset(FRAP.data, Reticular_vs_Focal == "Reticular")
Focal.FRAP.data <- subset(FRAP.data, Reticular_vs_Focal == "Focal")

Reticular.FRAP.data.LOESS <- loess(Standardised_b5_FRAP_recovery_relative_to_prebleach ~ Time, data = Reticular.FRAP.data, span = 0.2)
Focal.FRAP.data.LOESS <- loess(Standardised_b5_FRAP_recovery_relative_to_prebleach ~ Time, data = Focal.FRAP.data, span = 0.2)

Reticular.FRAP.data.LOESS.singular <- unique(Reticular.FRAP.data.LOESS$fitted)
Focal.FRAP.data.LOESS.singular <- unique(Focal.FRAP.data.LOESS$fitted)

KS.Reticular.vs.Focal.FRAP <- ks.test(Reticular.FRAP.data.LOESS.singular, Focal.FRAP.data.LOESS.singular, alternative = "two.sided")

print(KS.Reticular.vs.Focal.FRAP$p.value)
```

## Analysis of interphase STORM data

### STORM data wrangling1
```{r STORM data wrangling1}
## Determine CMAC.type (Reticular vs Focal depending on presence of A in $File (column))

CMAC.type = c()
for (cluster in 1:length(STORM.Data$File) ) {
  CMAC.type[cluster] <- if(grepl("A", STORM.Data$File[cluster]) == TRUE) {
    "Reticular"
  } else if(grepl("T", STORM.Data$File[cluster]) == TRUE) {
    "Focal"
  } else if(grepl("NR", STORM.Data$File[cluster]) == TRUE) {
    "Non-Retraction"
  } else {
    "Retraction"
  }
}

STORM.Data$CMAC.type <- cbind(as.character(CMAC.type))

## Unique nanocluster ID generation = merge of Folder, File and Cluster

# STORM.Data$Unique.ncID <- as.factor(paste(STORM.Data$Folder, STORM.Data$File, STORM.Data$Cluster))
STORM.Data$Unique.CMACID <- as.factor(paste(STORM.Data$Folder, STORM.Data$File))
STORM.Data <- as.data.frame(STORM.Data)
```

### Nearest Neighbour Analysis between localisations within unique nanoclusters
```{r Nearest Neighbour Analysis between localisations within unique nanoclusters}
## NND analysis of Incite3 data

NNND <- c()
for (i in 1:length(unique(STORM.Data$Unique.CMACID)) ) {
  temp = STORM.Data[STORM.Data$Unique.CMACID == unique(STORM.Data$Unique.CMACID)[i], ]
  NNND = append(NNND, nndist(temp[,7], temp[,8], k=1))
}

STORM.Data$NNND <- cbind(NNND)
```

### Boxplots summarising Nearest Neighbout Distance per nanocluster
```{r Boxplots summarising Nearest Neighbout Distance per nanocluster}
NNND.boxplot.filename <- "Panel 2V nearest neighbour boxplot"

NNND.A = subset(STORM.Data$NNND, CMAC.type == "Reticular")

NNND.T = subset(STORM.Data$NNND, CMAC.type == "Focal")


svglite(paste(panel_output_path, NNND.boxplot.filename, ".svg", sep = ""), width=6, height=4)

boxplot(NNND ~ CMAC.type, subset(STORM.Data, CMAC.type == "Reticular" | CMAC.type == "Focal"), notch = TRUE, outline = FALSE,
        main = "Nearest Neighbour Distance of Nanocluster by CMAC Type",
        xlab = "CMAC type", 
        ylab = "Nearest Neighbour Distance (nm)",
        plot = "TRUE"
)

dev.off()

boxplot(NNND ~ CMAC.type, subset(STORM.Data, CMAC.type == "Reticular" | CMAC.type == "Focal"), notch = TRUE, outline = FALSE,
        main = "Nearest Neighbour Distance of Nanocluster by CMAC Type",
        xlab = "CMAC type", 
        ylab = "Nearest Neighbour Distance (nm)",
        plot = "TRUE"
)

```

### Boxplots summarising Molecular Localization per nanocluster
```{r Boxplots summarising Molecular Localization per nanocluster}
Molecule.number.boxplot.filename <- "Panel 2W molecular number boxplot"

Molecules.A = as.numeric(subset(STORM.Data$Molecules, CMAC.type == "Reticular"))

Molecules.T = as.numeric(subset(STORM.Data$Molecules, CMAC.type == "Focal"))


svglite(paste(panel_output_path, Molecule.number.boxplot.filename, ".svg", sep = ""), width=6, height=4)

boxplot(as.numeric(Molecules) ~ CMAC.type, subset(STORM.Data, CMAC.type == "Reticular" | CMAC.type == "Focal"), notch = TRUE, outline = FALSE,
        main = "Number of Integrin avb5 Molecules by CMAC Type",
        xlab = "CMAC type", 
        ylab = "Number of Integrin avb5 Molecules",
        plot = "TRUE"
        )

dev.off()

boxplot(as.numeric(Molecules) ~ CMAC.type, subset(STORM.Data, CMAC.type == "Reticular" | CMAC.type == "Focal"), notch = TRUE, outline = FALSE,
        main = "Number of Integrin avb5 Molecules by CMAC Type",
        xlab = "CMAC type", 
        ylab = "Number of Integrin avb5 Molecules",
        plot = "TRUE"
        )

```

# Figure 3

## Import Data for Figure 3
```{r Import Data for Figure 3}
# Set your path to input data
data_input_path <- "/Users/johnlock/Dropbox/A-CMAC Manchester collaboration/NCB submission documents/Resubmission 2/Source_Data"
data_file <- "Supplementary Table 1 Statistics Source Data.xlsx"

# Import individual data files

CytoD2h_vs_control <- read_xlsx(paste(data_input_path, data_file, sep = "/"), sheet = "Fig3C", col_names = TRUE)
CytoD2h_vs_control <- CytoD2h_vs_control[,1:3]

Adhesion.Assay.Data <- read_xlsx(paste(data_input_path, data_file, sep = "/"), sheet = "Fig3E", col_names = TRUE)
Adhesion.Assay.Data <- Adhesion.Assay.Data[,1:4]

Talin.KD.Data <- read_xlsx(paste(data_input_path, data_file, sep = "/"), sheet = "Fig3H_I", col_names = TRUE)

```

## Adhesion Assay
### Fig 3E Plot Adhesion Levels per Condition
```{r Plot Adhesion Levels per Condition}
oldpar <- par()
par(mar = c(10, 2.5, 1, 1))

#define panel_file_name
Adhesion_Assay_panel_file_name <- "Panel 3E Adhesion Assay Boxplots"

Adhesion.Assay.Data$Condition <- factor(Adhesion.Assay.Data$Condition, unique(Adhesion.Assay.Data$Condition))

max_adhesion <- max(Adhesion.Assay.Data$NumberObjects)
Adhesion.Assay.Data$Normalised_Adhesion <- Adhesion.Assay.Data$NumberObjects/max_adhesion

svglite(paste(panel_output_path, Adhesion_Assay_panel_file_name, ".svg", sep = ""), width=7.7, height=6.5)

boxplot(Normalised_Adhesion ~ Condition, data = Adhesion.Assay.Data, notch = FALSE, varwidth = FALSE, outline = F, las=2, ylab = "Relative Cell Number", col = c("cs1wt -Cyto" = "#CB2327", "cs1wt +Cyto +RGD" = "#CB2327", "cs1b5 -Cyto" = "#4775A3", "cs1b5 -Cyto +RAD" = "#4775A3", "cs1b5 -Cyto +RGD" = "#9EC0E5", "cs1b5 +Cyto" = "#4775A3", "cs1b5 +Cyto +RAD" = "#4775A3", "cs1b5 +Cyto +RGD" = "#9EC0E5"), ylim = c(0,1.2))
beeswarm(Normalised_Adhesion ~ Condition, data = Adhesion.Assay.Data, method = 'center', add = T, col = "lightgrey", pch = 19, cex = 0.7)

dev.off()

boxplot(Normalised_Adhesion ~ Condition, data = Adhesion.Assay.Data, notch = FALSE, varwidth = FALSE, outline = F, las=2, ylab = "Relative Cell Number", col = c("cs1wt -Cyto" = "#CB2327", "cs1wt +Cyto +RGD" = "#CB2327", "cs1b5 -Cyto" = "#4775A3", "cs1b5 -Cyto +RAD" = "#4775A3", "cs1b5 -Cyto +RGD" = "#9EC0E5", "cs1b5 +Cyto" = "#4775A3", "cs1b5 +Cyto +RAD" = "#4775A3", "cs1b5 +Cyto +RGD" = "#9EC0E5"), ylim = c(0,1.2))
beeswarm(Normalised_Adhesion ~ Condition, data = Adhesion.Assay.Data, method = 'center', add = T, col = "lightgrey", pch = 19, cex = 0.7)


```

#### T-testing for Adhesion Level differences with Holm-Bonferroni p-value correction
```{r T-testing for Adhesion Level differences with Holm-Bonferroni p-value correction}

p_values <- c()
for (i in 1:length(unique(Adhesion.Assay.Data$Condition))){
  for (j in 1:length(unique(Adhesion.Assay.Data$Condition))){
    Condition_1 = subset(Adhesion.Assay.Data, Adhesion.Assay.Data$Condition == unique(Adhesion.Assay.Data$Condition)[i])
    Condition_1$Condition_Number = as.factor(i)
    Condition_2 = subset(Adhesion.Assay.Data, Adhesion.Assay.Data$Condition == unique(Adhesion.Assay.Data$Condition)[j])
    Condition_2$Condition_Number = as.factor(j+100)
    Condition_Pair = rbind(Condition_1, Condition_2)
    Condition_Pair_ttest = t.test(Normalised_Adhesion ~ Condition_Number, data = Condition_Pair, paired = FALSE)$p.value
    p_values = rbind(p_values, Condition_Pair_ttest)
  }
}

p_values <- as.vector(p_values)

p_value_matrix <- matrix(p_values,nrow = length(unique(Adhesion.Assay.Data$Condition)),ncol = length(unique(Adhesion.Assay.Data$Condition)))
columns <- as.vector(unique(Adhesion.Assay.Data$Condition))
rows <- as.vector(unique(Adhesion.Assay.Data$Condition))
p_value_dataframe <- as.data.frame(p_value_matrix, row.names = rows)
names(p_value_dataframe) <- columns

# print(p_value_dataframe)

# Introduce Holm (also called Holm-Bonferroni) p-value correction (used because it gives same stringency against false positives (type 1 errors) with lower probability of false negatives (type 2 errors)))

corrected_p_values <- p.adjust(p_values, method = "holm", n = length(p_values))

corrected_p_value_matrix1 <- matrix(corrected_p_values,nrow = length(unique(Adhesion.Assay.Data$Condition)),ncol = length(unique(Adhesion.Assay.Data$Condition)))
columns <- as.vector(unique(Adhesion.Assay.Data$Condition))
rows <- as.vector(unique(Adhesion.Assay.Data$Condition))
corrected_p_value_dataframe <- as.data.frame(corrected_p_value_matrix1, row.names = rows)
names(corrected_p_value_dataframe) <- columns

print(corrected_p_value_dataframe)

```

## Plot heatmap of corrected p_values
```{r Plot heatmap of corrected p_values, fig.height=4, fig.width=5}
heatmap.2(log(corrected_p_value_matrix1), dendrogram = 'none', Rowv = FALSE, Colv = FALSE, labRow = rows, labCol = columns, srtCol = 45, cexRow = 1, cexCol = 1, margins = c(7, 9), symm = TRUE, revC = FALSE, breaks = c(log(1e-300), log(1e-10), log(1e-6), log(0.001), log(0.01), log(0.05), log(1)), rowsep = 1:nrow(corrected_p_value_matrix1), colsep = 1:ncol(corrected_p_value_matrix1), sepcolor = 'darkgrey', sepwidth = c(0.02,0.02), trace = 'none', col = c("red", "orange", "green", "blue", "purple", "lightgrey"), denscol = NULL, keysize = 1.5, key.title = NA)
```


#### Wilcoxon Rank Sum testing for Adhesion Level differences
```{r Wilcoxon Rank Sum testing for Adhesion Level differences}

U_values <- c()
for (i in 1:length(unique(Adhesion.Assay.Data$Condition))){
  for (j in 1:length(unique(Adhesion.Assay.Data$Condition))){
    Condition_1 = subset(Adhesion.Assay.Data, Adhesion.Assay.Data$Condition == unique(Adhesion.Assay.Data$Condition)[i])
    Condition_1$Condition_Number = as.factor(i)
    Condition_2 = subset(Adhesion.Assay.Data, Adhesion.Assay.Data$Condition == unique(Adhesion.Assay.Data$Condition)[j])
    Condition_2$Condition_Number = as.factor(j+100)
    Condition_Pair = rbind(Condition_1, Condition_2)
    Condition_Pair_Utest = wilcox.test(Normalised_Adhesion ~ Condition_Number, data = Condition_Pair)$p.value
    U_values = rbind(p_values, Condition_Pair_Utest)
  }
}

U_values <- as.vector(U_values)

U_value_matrix <- matrix(U_values, nrow = length(unique(Adhesion.Assay.Data$Condition)),ncol = length(unique(Adhesion.Assay.Data$Condition)))
columns <- as.vector(unique(Adhesion.Assay.Data$Condition))
rows <- as.vector(unique(Adhesion.Assay.Data$Condition))
U_value_dataframe <- as.data.frame(U_value_matrix, row.names = rows)
names(U_value_dataframe) <- columns

print(U_value_dataframe)

```

## Talin KD effects
### Plot and test Talin KD effects on cell area and integrin b5 intensity within adhesions
```{r beeswarm plot of talin KD effects on cell spreading and b5 clustering}
# beeswarm(Total.Talin.per.Cell ~ siRNA..Oligo, data = Talin.KD.Data, method = 'center', col = "black", pch = 19, cex = 1, ylim = c(0,1))
# 
# beeswarm(Cell.Area ~ siRNA..Oligo, data = Talin.KD.Data, method = 'center', col = "black", pch = 19, cex = 1, ylim = c(0,1))
# 
# beeswarm(Mean.Clustered.b5.per.Cell ~ siRNA..Oligo, data = Talin.KD.Data, method = 'center', col = "black", pch = 19, cex = 1)#, ylim = c(0,1))

plot(log2(Talin.KD.Data$Total.Talin.per.Cell), Talin.KD.Data$Cell.Area, ylim = c(0,1), pch = c(0, 0, 0, 0, 2, 21), col = c("1", "1", "1", "1", "2", "3"))
abline(lm(Talin.KD.Data$Cell.Area ~ log2(Talin.KD.Data$Total.Talin.per.Cell)), col = "grey")

lm.cell.area_vs_talin.per.cell <- lm(Talin.KD.Data$Cell.Area ~ log2(Talin.KD.Data$Total.Talin.per.Cell))

TalinKD_Cell_Area_panel_file_name <- "Panel 3H TalinKD_vs_Cell_Area"

svglite(paste(panel_output_path, TalinKD_Cell_Area_panel_file_name, ".svg", sep = ""), width=3, height=3)
plot(log2(Talin.KD.Data$Total.Talin.per.Cell), Talin.KD.Data$Cell.Area, ylim = c(0,1), pch = c(0, 0, 0, 0, 2, 21), col = c("1", "1", "1", "1", "2", "3"), cex.axis = 0.5, cex.label = 0.5)
abline(lm(Talin.KD.Data$Cell.Area ~ log2(Talin.KD.Data$Total.Talin.per.Cell)), col = "grey")
dev.off()


plot(Talin.KD.Data$Total.Talin.per.Cell, log2(Talin.KD.Data$Mean.Clustered.b5.per.Cell), xlim = c(0,1), ylim = c(0,4), pch = c(0, 0, 0, 0, 2, 21),  col = c("1", "1", "1", "1", "2", "3"))
abline(lm(log2(Talin.KD.Data$Mean.Clustered.b5.per.Cell) ~ Talin.KD.Data$Total.Talin.per.Cell), col = "grey")

lm.b5.intensity_vs_talin.per.cell <- lm(log2(Talin.KD.Data$Mean.Clustered.b5.per.Cell) ~ Talin.KD.Data$Total.Talin.per.Cell)

TalinKD_b5_Intensity_panel_file_name <- "Panel 3I TalinKD_vs_b5_Intensity"
svglite(paste(panel_output_path, TalinKD_b5_Intensity_panel_file_name, ".svg", sep = ""), width=3, height=3)
plot(Talin.KD.Data$Total.Talin.per.Cell, log2(Talin.KD.Data$Mean.Clustered.b5.per.Cell), xlim = c(0,1), ylim = c(0,4), pch = c(0, 0, 0, 0, 2, 21),  col = c("1", "1", "1", "1", "2", "3"), cex.axis = 0.5, cex.label = 0.5)
abline(lm(log2(Talin.KD.Data$Mean.Clustered.b5.per.Cell) ~ Talin.KD.Data$Total.Talin.per.Cell), col = "grey")

dev.off()
```

## Test CytoD effects on adhesion intensities
#### Wilcoxon rank sum test of CytoD effect on vinculin and b5 intensity in adhesions
```{r Wilcoxon rank sum test of CytoD effect on vinculin and b5 intensity in adhesions}
names(CytoD2h_vs_control) <- c("Vinculin_Intensity", "b5_Intensity", "Condition")

Vinculin_Intensity_mu_test <- wilcox.test(Vinculin_Intensity ~ Condition, data = CytoD2h_vs_control)$p.value
print(Vinculin_Intensity_mu_test)

b5_Intensity_mu_test <- wilcox.test(b5_Intensity ~ Condition, data = CytoD2h_vs_control)$p.value
print(b5_Intensity_mu_test)
```

# Figure 5

## Import Data for Figure 5
```{r Import Data for Figure 5}
# Set your path to input data
data_input_path <- "/Users/johnlock/Dropbox/A-CMAC Manchester collaboration/NCB submission documents/Resubmission 2/Source_Data"
data_file <- "Supplementary Table 1 Statistics Source Data.xlsx"

# Import individual data files
Primary.siRNA.Screen.Data <-  read_xlsx(paste(data_input_path, data_file, sep = "/"), sheet = "Fig5B_C", col_names = TRUE)

Neo.LY.Drug.Treatment.Data <- read_xlsx(paste(data_input_path, data_file, sep = "/"), sheet = "Fig5D", col_names = TRUE)

```

## Analyse Primary siRNA Screen responses
### Boxplot changes in reticular to focal adhesion b5 intensity ratio due to siRNA
```{r Boxplot changes in reticular to focal adhesion b5 intensity ratio due to siRNA}
siRNA_boxplot_summary_filename <- "Panel 5B siRNA boxplot summary"

cell.numbers.primary.screen <- count(Primary.siRNA.Screen.Data, siRNA_Target)
count(Primary.siRNA.Screen.Data, siRNA_Target)
mean.cell.numbers.primary.screen <- summarise(cell.numbers.primary.screen, avg = mean(n), SD = sd(n), sum(n))
summarise(cell.numbers.primary.screen, avg = mean(n), SD = sd(n), sum(n))

ggplot(Primary.siRNA.Screen.Data, aes(x=siRNA_Target, y=Ratio_A.CMAC.to.T.CMAC_mean_intensity.z.score, color = factor(siRNA_Treatment_Type))) + 
  geom_boxplot(notch = TRUE, outlier.colour = "NA") + 
  scale_x_discrete(limits=c("Untreated","MOCK","SST_3Ctrl_Pool", "ON-TARGETplus Non-targeting Control",
                          "siGENOME Non-targeting Control", "ITGAV", "ITGB5", "OCRL", "PI4KA",
                          "PI4K2B", "PTEN", "PIP5K1B", "PIP5K1C", "PIK3C2A")) +
  theme(axis.text.x  = element_text(angle=90)) +
  ylim(-3, 6)#+

svglite(paste(panel_output_path, siRNA_boxplot_summary_filename, ".svg", sep = ""), width=7, height=5)

ggplot(Primary.siRNA.Screen.Data, aes(x=siRNA_Target, y=Ratio_A.CMAC.to.T.CMAC_mean_intensity.z.score, color = factor(siRNA_Treatment_Type))) + 
  geom_boxplot(notch = TRUE, outlier.colour = "NA") + 
  scale_x_discrete(limits=c("Untreated","MOCK","SST_3Ctrl_Pool", "ON-TARGETplus Non-targeting Control",
                          "siGENOME Non-targeting Control", "ITGAV", "ITGB5", "OCRL", "PI4KA",
                          "PI4K2B", "PTEN", "PIP5K1B", "PIP5K1C", "PIK3C2A")) +
  theme(axis.text.x  = element_text(angle=90)) +
  ylim(-3, 6)#+

dev.off()

```

### Parallel coordinates plot of adhesion responses to siRNA
```{r Parallel coordinates plot of adhesion responses to siRNA}
siRNA_parcoord_summary_filename <- "Panel 5C siRNA parcoord summary"

Primary_screen_siRNA_subset <- subset(Primary.siRNA.Screen.Data, siRNA_Target == "Untreated" | siRNA_Target == "MOCK" | siRNA_Target == "SST_3Ctrl_Pool" | siRNA_Target ==  "ON-TARGETplus Non-targeting Control" | siRNA_Target == "siGENOME Non-targeting Control" | siRNA_Target ==  "OCRL" | siRNA_Target ==  "PI4KA" | siRNA_Target == "PI4K2B" | siRNA_Target ==  "PTEN" | siRNA_Target ==  "PIP5K1B" | siRNA_Target ==  "PIP5K1C" | siRNA_Target ==  "PIK3C2A")

Primary_screen_siRNA_subset_grouped <- aggregate(Primary_screen_siRNA_subset[, c(11,13,18)], list(Primary_screen_siRNA_subset$siRNA_Target), median)

Primary_screen_siRNA_subset_grouped$Group.1 <- 
  as.factor(Primary_screen_siRNA_subset_grouped$Group.1)

names(Primary_screen_siRNA_subset_grouped) <- c("siRNA_Target", "Mean_Reticular_Adhesion_Intensity", "Mean_Focal_Adhesion_Intensity", "Ratio_Reticular_Focal_Adhesion_Intensity")

svglite(paste(panel_output_path, siRNA_parcoord_summary_filename, ".svg", sep = ""), width=7, height=5)

ggparcoord(Primary_screen_siRNA_subset_grouped, columns = c(2,3,4), groupColumn = 1, scale = "globalminmax", order = c(2,3,4)) + theme(axis.text.x  = element_text(angle=60, hjust = 1, size = 5))

dev.off()

ggparcoord(Primary_screen_siRNA_subset_grouped, columns = c(2,3,4), groupColumn = 1, scale = "globalminmax", order = c(2,3,4)) + theme(axis.text.x  = element_text(angle=60, hjust = 1, size = 5))

```


### Mann-Whitney test for significant siRNA treatment effects
```{r Mann-Whitney test for significant siRNA treatment effects}
siRNA.conditions.for.U.test <- subset(Primary.siRNA.Screen.Data, siRNA_Target != "Untreated" & siRNA_Target != "MOCK" & siRNA_Target != "SST_3Ctrl_Pool" & siRNA_Target != "siGENOME Non-targeting Control") 

p_values <- c()
for (i in 1:length(unique(siRNA.conditions.for.U.test$siRNA_Target))){
  for (j in 1:length(unique(siRNA.conditions.for.U.test$siRNA_Target))){
    siRNA_Target_1 = subset(siRNA.conditions.for.U.test, siRNA.conditions.for.U.test$siRNA_Target == unique(siRNA.conditions.for.U.test$siRNA_Target)[i])
    siRNA_Target_1$siRNA_Target_Number = as.factor(i)
    siRNA_Target_2 = subset(siRNA.conditions.for.U.test, siRNA.conditions.for.U.test$siRNA_Target == unique(siRNA.conditions.for.U.test$siRNA_Target)[j])
    siRNA_Target_2$siRNA_Target_Number = as.factor(j+100)
    siRNA_Target_Pair = rbind(siRNA_Target_1, siRNA_Target_2)
    siRNA_Target_Pair_ttest = wilcox.test(Ratio_A.CMAC.to.T.CMAC_mean_intensity.z.score ~ siRNA_Target_Number, data = siRNA_Target_Pair)$p.value
    p_values = rbind(p_values, siRNA_Target_Pair_ttest)
  }
}

p_values <- as.vector(p_values)

p_value_matrix <- matrix(p_values,nrow = length(unique(siRNA.conditions.for.U.test$siRNA_Target)),ncol = length(unique(siRNA.conditions.for.U.test$siRNA_Target)))
columns <- as.vector(unique(siRNA.conditions.for.U.test$siRNA_Target))
rows <- as.vector(unique(siRNA.conditions.for.U.test$siRNA_Target))
p_value_dataframe <- as.data.frame(p_value_matrix, row.names = rows)
names(p_value_dataframe) <- columns

# print(p_value_dataframe)

# Introduce Holm (also called Holm-Bonferroni) p-value correction (used because it gives same stringency against false positives (type 1 errors) with lower probability of false negatives (type 2 errors)))

corrected_p_values <- p.adjust(p_values, method = "holm", n = length(p_values))

corrected_p_value_matrix2 <- matrix(corrected_p_values,nrow = length(unique(siRNA.conditions.for.U.test$siRNA_Target)),ncol = length(unique(siRNA.conditions.for.U.test$siRNA_Target)))
columns <- as.vector(unique(siRNA.conditions.for.U.test$siRNA_Target))
rows <- as.vector(unique(siRNA.conditions.for.U.test$siRNA_Target))
corrected_p_value_dataframe <- as.data.frame(corrected_p_value_matrix2, row.names = rows)
names(corrected_p_value_dataframe) <- columns

print(corrected_p_value_dataframe)

```

### Plot heatmap of corrected p_values mann-whitney
```{r Plot heatmap of corrected p_values mann-whitney, fig.height=6, fig.width=6}
heatmap.2(log(corrected_p_value_matrix2), dendrogram = 'none', Rowv = FALSE, Colv = FALSE, labRow = rows, labCol = columns, srtCol = 45, cexRow = 1, cexCol = 1, margins = c(7, 9), symm = TRUE, revC = FALSE, breaks = c(log(1e-300), log(1e-50), log(1e-10), log(1e-6), log(0.001), log(0.01), log(0.05), log(1)), rowsep = 1:nrow(corrected_p_value_matrix2), colsep = 1:ncol(corrected_p_value_matrix2), sepcolor = 'darkgrey', sepwidth = c(0.02,0.02), trace = 'none', col = c("darkred", "red", "orange", "green", "blue", "purple", "lightgrey"), denscol = NULL, keysize = 1.5, key.title = NA)
```

## Analyse Neomycin and LY drug treatment responses

### Boxplots of b5 adhesion intensity changes due to neomycin or LY
```{r Boxplots of b5 adhesion intensity changes due to neomycin or LY}
PIP_drug_boxplot_summary_filename <- "Panel 5D PIP drug boxplot summary"

cell.numbers.NEO_LY.screen <- count(Neo.LY.Drug.Treatment.Data, Drug)
count(Neo.LY.Drug.Treatment.Data, Drug)
mean.cell.numbers.NEO_LY.screen <- summarise(cell.numbers.NEO_LY.screen, avg = mean(n), SD = sd(n), sum(n))
summarise(cell.numbers.NEO_LY.screen, avg = mean(n), SD = sd(n), sum(n))

svglite(paste(panel_output_path, PIP_drug_boxplot_summary_filename, ".svg", sep = ""), width=7, height=5)

p1 <- ggplot(Neo.LY.Drug.Treatment.Data, aes(x=Drug, y=Median.Mean..z.score, color = factor(Drug))) + 
  geom_boxplot(notch = TRUE, outlier.colour = "NA") + 
  scale_x_discrete(limits=c("DMSO", "Neo_10mM","LY_25mM")) +
  theme(axis.text.x  = element_text(angle=45, vjust = 0.5)) +
  coord_cartesian(ylim=c(-3, 6)) + ylab("Mean Reticular Adhesion Intensity") + theme(legend.position="none")


## plot T-CMAC intensity Z-score ggplot2 boxplot - per Drug 

p2 <- ggplot(Neo.LY.Drug.Treatment.Data, aes(x=Drug, y=Median.Mean._T.CMACs.z.score, color = factor(Drug))) + 
  geom_boxplot(notch = TRUE, outlier.colour = "NA") + 
  scale_x_discrete(limits=c("DMSO", "Neo_10mM","LY_25mM")) +
  theme(axis.text.x  = element_text(angle=45, vjust = 0.5)) +
  coord_cartesian(ylim=c(-3, 6)) + ylab("Mean Focal Adhesion Intensity") + theme(legend.position="none")

## plot intensity ratio Z-score ggplot2 boxplot - per Drug 

p3 <- ggplot(Neo.LY.Drug.Treatment.Data, aes(x=Drug, y=Ratio_A.CMAC.to.T.CMAC_mean_intensity.z.score, color = factor(Drug))) + 
  geom_boxplot(notch = TRUE, outlier.colour = "NA") + 
  scale_x_discrete(limits=c("DMSO", "Neo_10mM","LY_25mM")) +
  theme(axis.text.x  = element_text(angle=45, vjust = 0.5)) +
  coord_cartesian(ylim=c(-3, 6)) + ylab("Ratio Reticular:Focal Adhesion Intensity") + theme(legend.position="none")

grid.arrange(p1, p2, p3, ncol = 3)

dev.off()

p1 <- ggplot(Neo.LY.Drug.Treatment.Data, aes(x=Drug, y=Median.Mean..z.score, color = factor(Drug))) + 
  geom_boxplot(notch = TRUE, outlier.colour = "NA") + 
  scale_x_discrete(limits=c("DMSO", "Neo_10mM","LY_25mM")) +
  theme(axis.text.x  = element_text(angle=45, vjust = 0.5)) +
  coord_cartesian(ylim=c(-3, 6)) + ylab("Mean Reticular Adhesion Intensity") + theme(legend.position="none")


## plot T-CMAC intensity Z-score ggplot2 boxplot - per Drug 

p2 <- ggplot(Neo.LY.Drug.Treatment.Data, aes(x=Drug, y=Median.Mean._T.CMACs.z.score, color = factor(Drug))) + 
  geom_boxplot(notch = TRUE, outlier.colour = "NA") + 
  scale_x_discrete(limits=c("DMSO", "Neo_10mM","LY_25mM")) +
  theme(axis.text.x  = element_text(angle=45, vjust = 0.5)) +
  coord_cartesian(ylim=c(-3, 6)) + ylab("Mean Focal Adhesion Intensity") + theme(legend.position="none")

## plot intensity ratio Z-score ggplot2 boxplot - per Drug 

p3 <- ggplot(Neo.LY.Drug.Treatment.Data, aes(x=Drug, y=Ratio_A.CMAC.to.T.CMAC_mean_intensity.z.score, color = factor(Drug))) + 
  geom_boxplot(notch = TRUE, outlier.colour = "NA") + 
  scale_x_discrete(limits=c("DMSO", "Neo_10mM","LY_25mM")) +
  theme(axis.text.x  = element_text(angle=45, vjust = 0.5)) +
  coord_cartesian(ylim=c(-3, 6)) + ylab("Ratio Reticular:Focal Adhesion Intensity") + theme(legend.position="none")

grid.arrange(p1, p2, p3, ncol = 3)


```

### Mann-Whitney test for significant Drug treatment effects on Reticular adhesion intensity
```{r Mann-Whitney test for significant Drug treatment effects on Reticular adhesion intensity}
p_values <- c()
for (i in 1:length(unique(Neo.LY.Drug.Treatment.Data$Drug))){
  for (j in 1:length(unique(Neo.LY.Drug.Treatment.Data$Drug))){
    Drug_1 = subset(Neo.LY.Drug.Treatment.Data, Neo.LY.Drug.Treatment.Data$Drug == unique(Neo.LY.Drug.Treatment.Data$Drug)[i])
    Drug_1$Drug_Number = as.factor(i)
    Drug_2 = subset(Neo.LY.Drug.Treatment.Data, Neo.LY.Drug.Treatment.Data$Drug == unique(Neo.LY.Drug.Treatment.Data$Drug)[j])
    Drug_2$Drug_Number = as.factor(j+100)
    Drug_Pair = rbind(Drug_1, Drug_2)
    Drug_Pair_ttest = wilcox.test(Median.Mean..z.score ~ Drug_Number, data = Drug_Pair)$p.value
    p_values = rbind(p_values, Drug_Pair_ttest)
  }
}

p_values <- as.vector(p_values)

p_value_matrix <- matrix(p_values,nrow = length(unique(Neo.LY.Drug.Treatment.Data$Drug)),ncol = length(unique(Neo.LY.Drug.Treatment.Data$Drug)))
columns <- as.vector(unique(Neo.LY.Drug.Treatment.Data$Drug))
rows <- as.vector(unique(Neo.LY.Drug.Treatment.Data$Drug))
p_value_dataframe <- as.data.frame(p_value_matrix, row.names = rows)
names(p_value_dataframe) <- columns

# print(p_value_dataframe)

# Introduce Holm (also called Holm-Bonferroni) p-value correction (used because it gives same stringency against false positives (type 1 errors) with lower probability of false negatives (type 2 errors)))

corrected_p_values <- p.adjust(p_values, method = "holm", n = length(p_values))

corrected_p_value_matrix3 <- matrix(corrected_p_values,nrow = length(unique(Neo.LY.Drug.Treatment.Data$Drug)),ncol = length(unique(Neo.LY.Drug.Treatment.Data$Drug)))
columns <- as.vector(unique(Neo.LY.Drug.Treatment.Data$Drug))
rows <- as.vector(unique(Neo.LY.Drug.Treatment.Data$Drug))
corrected_p_value_dataframe <- as.data.frame(corrected_p_value_matrix3, row.names = rows)
names(corrected_p_value_dataframe) <- columns

print(corrected_p_value_dataframe)
```

### Mann-Whitney test for significant Drug treatment effects on Focal adhesion intensity
```{r Mann-Whitney test for significant Drug treatment effects on Focal adhesion intensity}
p_values <- c()
for (i in 1:length(unique(Neo.LY.Drug.Treatment.Data$Drug))){
  for (j in 1:length(unique(Neo.LY.Drug.Treatment.Data$Drug))){
    Drug_1 = subset(Neo.LY.Drug.Treatment.Data, Neo.LY.Drug.Treatment.Data$Drug == unique(Neo.LY.Drug.Treatment.Data$Drug)[i])
    Drug_1$Drug_Number = as.factor(i)
    Drug_2 = subset(Neo.LY.Drug.Treatment.Data, Neo.LY.Drug.Treatment.Data$Drug == unique(Neo.LY.Drug.Treatment.Data$Drug)[j])
    Drug_2$Drug_Number = as.factor(j+100)
    Drug_Pair = rbind(Drug_1, Drug_2)
    Drug_Pair_ttest = wilcox.test(Median.Mean._T.CMACs.z.score ~ Drug_Number, data = Drug_Pair)$p.value
    p_values = rbind(p_values, Drug_Pair_ttest)
  }
}

p_values <- as.vector(p_values)

p_value_matrix <- matrix(p_values,nrow = length(unique(Neo.LY.Drug.Treatment.Data$Drug)),ncol = length(unique(Neo.LY.Drug.Treatment.Data$Drug)))
columns <- as.vector(unique(Neo.LY.Drug.Treatment.Data$Drug))
rows <- as.vector(unique(Neo.LY.Drug.Treatment.Data$Drug))
p_value_dataframe <- as.data.frame(p_value_matrix, row.names = rows)
names(p_value_dataframe) <- columns

# print(p_value_dataframe)

# Introduce Holm (also called Holm-Bonferroni) p-value correction (used because it gives same stringency against false positives (type 1 errors) with lower probability of false negatives (type 2 errors)))

corrected_p_values <- p.adjust(p_values, method = "holm", n = length(p_values))

corrected_p_value_matrix4 <- matrix(corrected_p_values,nrow = length(unique(Neo.LY.Drug.Treatment.Data$Drug)),ncol = length(unique(Neo.LY.Drug.Treatment.Data$Drug)))
columns <- as.vector(unique(Neo.LY.Drug.Treatment.Data$Drug))
rows <- as.vector(unique(Neo.LY.Drug.Treatment.Data$Drug))
corrected_p_value_dataframe <- as.data.frame(corrected_p_value_matrix4, row.names = rows)
names(corrected_p_value_dataframe) <- columns

print(corrected_p_value_dataframe)
```
### Mann-Whitney test for significant Drug treatment effects on intensity ratio Z-score
```{r Mann-Whitney test for significant Drug treatment effects on intensity ratio Z-score}
p_values <- c()
for (i in 1:length(unique(Neo.LY.Drug.Treatment.Data$Drug))){
  for (j in 1:length(unique(Neo.LY.Drug.Treatment.Data$Drug))){
    Drug_1 = subset(Neo.LY.Drug.Treatment.Data, Neo.LY.Drug.Treatment.Data$Drug == unique(Neo.LY.Drug.Treatment.Data$Drug)[i])
    Drug_1$Drug_Number = as.factor(i)
    Drug_2 = subset(Neo.LY.Drug.Treatment.Data, Neo.LY.Drug.Treatment.Data$Drug == unique(Neo.LY.Drug.Treatment.Data$Drug)[j])
    Drug_2$Drug_Number = as.factor(j+100)
    Drug_Pair = rbind(Drug_1, Drug_2)
    Drug_Pair_ttest = wilcox.test(as.numeric(Ratio_A.CMAC.to.T.CMAC_mean_intensity.z.score) ~ Drug_Number, data = Drug_Pair)$p.value 
    p_values = rbind(p_values, Drug_Pair_ttest)
  }
}

p_values <- as.vector(p_values)

p_value_matrix <- matrix(p_values,nrow = length(unique(Neo.LY.Drug.Treatment.Data$Drug)),ncol = length(unique(Neo.LY.Drug.Treatment.Data$Drug)))
columns <- as.vector(unique(Neo.LY.Drug.Treatment.Data$Drug))
rows <- as.vector(unique(Neo.LY.Drug.Treatment.Data$Drug))
p_value_dataframe <- as.data.frame(p_value_matrix, row.names = rows)
names(p_value_dataframe) <- columns

# print(p_value_dataframe)

# Introduce Holm (also called Holm-Bonferroni) p-value correction (used because it gives same stringency against false positives (type 1 errors) with lower probability of false negatives (type 2 errors)))

corrected_p_values <- p.adjust(p_values, method = "holm", n = length(p_values))

corrected_p_value_matrix5 <- matrix(corrected_p_values,nrow = length(unique(Neo.LY.Drug.Treatment.Data$Drug)),ncol = length(unique(Neo.LY.Drug.Treatment.Data$Drug)))
columns <- as.vector(unique(Neo.LY.Drug.Treatment.Data$Drug))
rows <- as.vector(unique(Neo.LY.Drug.Treatment.Data$Drug))
corrected_p_value_dataframe <- as.data.frame(corrected_p_value_matrix5, row.names = rows)
names(corrected_p_value_dataframe) <- columns

print(corrected_p_value_dataframe)
```
# Figure 6

## Import Data for Figure 6
```{r mport Data for Figure 6}

# Set your path to input data
data_input_path <- "/Users/johnlock/Dropbox/A-CMAC Manchester collaboration/NCB submission documents/Resubmission 2/Source_Data"
data_file <- "Supplementary Table 1 Statistics Source Data.xlsx"

# Import individual data files

Proliferation <- read_xlsx(paste(data_input_path, data_file, sep = "/"), sheet = "Fig6A", col_names = TRUE)
Proliferation$Day <- as.factor(Proliferation$Day)


EDU_Incorporation_Percentage <-  read_xlsx(paste(data_input_path, data_file, sep = "/"), sheet = "Fig6B", col_names = TRUE)
EDU_Incorporation_Percentage <- EDU_Incorporation_Percentage[,1:2]

```

## Summarise Proliferation data per Condition per Day
```{r Summarise Proliferation data per Condition per Day}
Prol_Sum <- Proliferation %>%
  group_by(.dots = c('Condition', 'Day')) %>%
  summarise(
    mean_proliferation = mean(Relative.Proliferation, na.rm = TRUE),
    SD_proliferation = sd(Relative.Proliferation, na.rm = TRUE)
  )
```

### Plot Cell Proliferation Comparison of control vs b5KD
```{r Plot Cell Proliferation Comparison of control vs b5KD}
proliferation_linechart_filename <- "Panel 6A proliferation linechart"

# The errorbars overlapped, so use position_dodge to move them horizontally
pd <- position_dodge(0.1) # move them .05 to the left and right

svglite(paste(panel_output_path, proliferation_linechart_filename, ".svg", sep = ""), width=4, height=3)

ggplot(Prol_Sum, aes(x=Day, y=mean_proliferation, colour=Condition, group=Condition)) + 
    geom_errorbar(aes(ymin=mean_proliferation-SD_proliferation, ymax=mean_proliferation+SD_proliferation), width=.1, position=pd) +
  scale_color_manual(values = c("Control" = "#49494A", "b5KD" = "#EC1C24")) +
    geom_line(position=pd, size = 1) +
    geom_point(position=pd, size=3, shape=19) + # 21 is filled circle
    xlab("Day") +
    ylab("Relative Proliferation") +
    theme_light()

dev.off()

ggplot(Prol_Sum, aes(x=Day, y=mean_proliferation, colour=Condition, group=Condition)) + 
    geom_errorbar(aes(ymin=mean_proliferation-SD_proliferation, ymax=mean_proliferation+SD_proliferation), width=.1, position=pd) +
  scale_color_manual(values = c("Control" = "#49494A", "b5KD" = "#EC1C24")) +
    geom_line(position=pd, size = 1) +
    geom_point(position=pd, size=3, shape=19) + # 21 is filled circle
    xlab("Day") +
    ylab("Relative Proliferation") +
    theme_light()

```

### T-testing Cell Proliferation Comparison per Condition per Day
```{r T-testing Cell Proliferation Comparison per Condition per Day}

Proliferation$Day <- as.factor(Proliferation$Day)

p_values <- c()
for (i in 1:length(unique(Proliferation$Day))){
  
    single_day_data = subset(Proliferation, Proliferation$Day == unique(Proliferation$Day)[i])
    
    Control_vs_b5KD_per_day_ttest = t.test(Relative.Proliferation ~ Condition, data = single_day_data, paired = FALSE)$p.value
    p_values = rbind(p_values, Control_vs_b5KD_per_day_ttest)

}

p_values <- as.vector(p_values)

p_value_matrix <- matrix(p_values,nrow = 1,ncol = length(p_values))
columns <- as.vector(paste("Day", unique(Proliferation$Day), sep = " "))
rows <- paste(unique(Proliferation$Condition)[1], unique(Proliferation$Condition)[2], sep = "_vs_")
p_value_dataframe <- as.data.frame(p_value_matrix, row.names = rows)
names(p_value_dataframe) <- columns

print(p_value_dataframe)

```


## EDU Incorporation Comparison per Condition
### Fig 6B Plot EDU Incorporation Boxplot per Condition
```{r Plot EDU Incorporation Boxplot per Condition}

#define panel_file_name
EDU_boxplot_panel_file_name <- "Panel 6B EDU Incorporation Boxplots"

EDU_Incorporation_Percentage$Condition <- factor(EDU_Incorporation_Percentage$Condition, c("Control", "b5KD"))

svglite(paste(panel_output_path, EDU_boxplot_panel_file_name, ".svg", sep = ""), width=4, height=6)

boxplot(Percentage.EDU.positive.Cells.per.image ~ Condition, data = EDU_Incorporation_Percentage, notch = T, varwidth = T, outline = F, ylab = "% EDU positive cells", col = c(control = "#49494A", b5KD = "#EC1C24"), ylim = c(0,115))
beeswarm(Percentage.EDU.positive.Cells.per.image ~ Condition, data = EDU_Incorporation_Percentage, method = 'center', add = T, col = "steelblue2", pch = 1, cex = 1.3)

dev.off()

boxplot(Percentage.EDU.positive.Cells.per.image ~ Condition, data = EDU_Incorporation_Percentage, notch = T, varwidth = T, outline = F, ylab = "% EDU positive cells", col = c(control = "#49494A", b5KD = "#EC1C24"), ylim = c(0,115))
beeswarm(Percentage.EDU.positive.Cells.per.image ~ Condition, data = EDU_Incorporation_Percentage, method = 'center', add = T, col = "steelblue2", pch = 1, cex = 1.3)


```

#### Fig6B Mann-Whitney µ test for EDU Incorporation differences
```{r Mann-Whitney µ test for EDU Incorporation differences}

#Note: Equivalent to Wilcoxon rank sum test as data is unpaired

Utest.b5KD.vs.control_EDU <- wilcox.test(Percentage.EDU.positive.Cells.per.image ~ Condition, data = EDU_Incorporation_Percentage)
Mann_Whitney_EDU_Incorporation_Score <- Utest.b5KD.vs.control_EDU$p.value

print(Mann_Whitney_EDU_Incorporation_Score)

```


# Figure 7

## Import Data for Figure 7
```{r mport Data for Figure 7}

# Set your path to input data
data_input_path <- "/Users/johnlock/Dropbox/A-CMAC Manchester collaboration/NCB submission documents/Resubmission 2/Source_Data"
data_file <- "Supplementary Table 1 Statistics Source Data.xlsx"

# Import individual data files

STORM.Data <- read_xlsx(paste(data_input_path, data_file, sep = "/"), sheet = "Fig7E_F", col_names = TRUE)

```

## Analysis of mitotic STORM data

### STORM data wrangling2
```{r STORM data wrangling2}
## Determine CMAC.type (Reticular vs Focal depending on presence of A in $File (column))

CMAC.type = c()
for (cluster in 1:length(STORM.Data$File) ) {
  CMAC.type[cluster] <- if(grepl("A", STORM.Data$File[cluster]) == TRUE) {
    "Reticular"
  } else if(grepl("T", STORM.Data$File[cluster]) == TRUE) {
    "Focal"
  } else if(grepl("NR", STORM.Data$File[cluster]) == TRUE) {
    "Non-Retraction"
  } else {
    "Retraction"
  }
}

STORM.Data$CMAC.type <- cbind(as.character(CMAC.type))

## Unique nanocluster ID generation = merge of Folder, File and Cluster

STORM.Data$Unique.ncID <- as.factor(paste(STORM.Data$Folder, STORM.Data$File, STORM.Data$Cluster))
STORM.Data$Unique.CMACID <- as.factor(paste(STORM.Data$Folder, STORM.Data$File))
```

### Nearest Neighbour Analysis between localisations within unique nanoclusters2
```{r Nearest Neighbour Analysis between localisations within unique nanoclusters2}
## NND analysis of Incite3 data

NNND <- c()
for (i in 1:length(unique(STORM.Data$Unique.CMACID)) ) {
  temp = STORM.Data[STORM.Data$Unique.CMACID == unique(STORM.Data$Unique.CMACID)[i], ]
  NNND = append(NNND, nndist(temp$Centroid.nm., temp$Centroid.nm..1, k=1))
}

STORM.Data$NNND <- cbind(NNND)
```

### Boxplots summarising Nearest Neighbout Distance per nanocluster2
```{r Boxplots summarising Nearest Neighbout Distance per nanocluster2}

NNND.A = subset(STORM.Data$NNND, CMAC.type == "Non-Retraction")

NNND.T = subset(STORM.Data$NNND, CMAC.type == "Retraction")


TEST_nnnd_NRvsR <- wilcox.test(NNND.A, NNND.T, paired = FALSE)

NNND_boxplot_filename <- "Panel 7E NNND boxplot"

svglite(paste(panel_output_path, NNND_boxplot_filename, ".svg", sep = ""), width=4, height=3)

boxplot(NNND ~ CMAC.type, subset(STORM.Data, CMAC.type == "Non-Retraction" | CMAC.type == "Retraction"), notch = TRUE, outline = FALSE,
        main = "Nearest Neighbour Distance of Nanocluster by CMAC Type",
        sub = TEST_nnnd_NRvsR$p.value,
        xlab = "CMAC type", 
        ylab = "Nearest Neighbour Distance (nm)",
        plot = "TRUE"
)

dev.off()

boxplot(NNND ~ CMAC.type, subset(STORM.Data, CMAC.type == "Non-Retraction" | CMAC.type == "Retraction"), notch = TRUE, outline = FALSE,
        main = "Nearest Neighbour Distance of Nanocluster by CMAC Type",
        sub = TEST_nnnd_NRvsR$p.value,
        xlab = "CMAC type", 
        ylab = "Nearest Neighbour Distance (nm)",
        plot = "TRUE"
)
```

### Boxplots summarising Molecular Localization per nanocluster2
```{r Boxplots summarising Molecular Localization per nanocluster2}
STORM.Data$Molecules <- as.numeric(STORM.Data$Molecules)

Molecules.A = subset(STORM.Data$Molecules, CMAC.type == "Non-Retraction")

Molecules.T = subset(STORM.Data$Molecules, CMAC.type == "Retraction")


Mol_Count_boxplot_filename <- "Panel 7F Molecule Count boxplot"

svglite(paste(panel_output_path, Mol_Count_boxplot_filename, ".svg", sep = ""), width=4, height=3)

TEST_Molecules.A_RetvsFoc <- wilcox.test(Molecules.A, Molecules.T, paired = FALSE)
boxplot(Molecules ~ CMAC.type, subset(STORM.Data, CMAC.type == "Non-Retraction" | CMAC.type == "Retraction"), notch = TRUE,outline = FALSE,
        main = "Number of Integrin avb5 Molecules by CMAC Type",
        sub = TEST_Molecules.A_RetvsFoc$p.value,
        xlab = "CMAC type", 
        ylab = "Number of Integrin avb5 Molecules",
        plot = "TRUE"
        )

dev.off()

TEST_Molecules.A_RetvsFoc <- wilcox.test(Molecules.A, Molecules.T, paired = FALSE)
boxplot(Molecules ~ CMAC.type, subset(STORM.Data, CMAC.type == "Non-Retraction" | CMAC.type == "Retraction"), notch = TRUE,outline = FALSE,
        main = "Number of Integrin avb5 Molecules by CMAC Type",
        sub = TEST_Molecules.A_RetvsFoc$p.value,
        xlab = "CMAC type", 
        ylab = "Number of Integrin avb5 Molecules",
        plot = "TRUE"
        )

```

# Figure 8

## Import Data for Figure 8
```{r mport Data for Figure 8}

# Set your path to input data
data_input_path <- "/Users/johnlock/Dropbox/A-CMAC Manchester collaboration/NCB submission documents/Resubmission 2/Source_Data"
data_file <- "Supplementary Table 1 Statistics Source Data.xlsx"

# Import individual data files

Residual_Angle_data <- read_xlsx(paste(data_input_path, data_file, sep = "/"), sheet = "Fig8A_B", col_names = TRUE)

Cell_Division_Defect_Quantification <- read_xlsx(paste(data_input_path, data_file, sep = "/"), sheet = "Fig8C", col_names = TRUE)
Cell_Division_Defect_Quantification <- Cell_Division_Defect_Quantification[1:9, 1:4]

```

## Residual Angle Comparisons per Condition
### Fig 8A Plot Residual Angle Boxplot of +/- Beeswarm by condition
```{r Plot Residual Angle Boxplot by condition}

#define panel_file_name
boxplot_panel_file_name <- "Panel 8A Residual Angle Boxplots"

# oldpar <- par()
# par(mfrow = c(1,1), mar = c(5,5,1,1))

Residual_Angle_dataCN <- Residual_Angle_data

# Set Condition order
Residual_Angle_dataCN$Condition <- factor(Residual_Angle_dataCN$Condition, c("Control", "b5KD", "Rescue"))

svglite(paste(panel_output_path, boxplot_panel_file_name, ".svg", sep = ""), width=4, height=6)

boxplot(Pre.to.Post.Mitosis.Angle ~ Condition, data = Residual_Angle_dataCN, notch = T, varwidth = T, outline = F, ylab = "Residual Angle", col = c(control = "#49494A", b5KD = "#EC1C24", Rescue = "#BCBCBC"), ylim = c(0,115))
beeswarm(Pre.to.Post.Mitosis.Angle ~ Condition, data = Residual_Angle_dataCN, method = 'center', add = T, col = "steelblue2", pch = 1, cex = 0.5)

dev.off()

boxplot(Pre.to.Post.Mitosis.Angle ~ Condition, data = Residual_Angle_dataCN, notch = T, varwidth = T, outline = F, ylab = "Residual Angle", col = c(control = "#49494A", b5KD = "#EC1C24", Rescue = "#BCBCBC"), ylim = c(0,115))
beeswarm(Pre.to.Post.Mitosis.Angle ~ Condition, data = Residual_Angle_dataCN, method = 'center', add = T, col = "steelblue2", pch = 1, cex = 0.5)

```


### Fig 8B Plot Residual Angle smoothed density plot per condition
```{r Plot Residual Angle smoothed density plot per condition}

#define panel_file_name
density_panel_file_name <- "Panel 8B Residual Angle Density Plot"

svglite(paste(panel_output_path, density_panel_file_name, ".svg", sep = ""), width=4, height=6)

sm.density.compare(Residual_Angle_dataCN$Pre.to.Post.Mitosis.Angle, Residual_Angle_dataCN$Condition, col = c("#49494A", "#EC1C24", "#BCBCBC"), lty = c(1,1,1), lwd = 2, h = 5, xlim = c(0,90), xlab = "Residual Angle")
colfill<-c("#49494A", "#EC1C24", "#BCBCBC")
legend(x=40, y=0.033, levels(Residual_Angle_dataCN$Condition), fill=colfill, bty = "n")

dev.off()

sm.density.compare(Residual_Angle_dataCN$Pre.to.Post.Mitosis.Angle, Residual_Angle_dataCN$Condition, col = c("#49494A", "#EC1C24", "#BCBCBC"), lty = c(1,1,1), lwd = 2, h = 5, xlim = c(0,90), xlab = "Residual Angle")
colfill<-c("#49494A", "#EC1C24", "#BCBCBC")
legend(x=40, y=0.033, levels(Residual_Angle_dataCN$Condition), fill=colfill, bty = "n")

```

#### Count cell number per condition
```{r Count cell number per condition}
Residual_Angle_dataCN %>%
  group_by(Condition) %>%
  summarise(
    n = n())
```


#### Mann-Whitney µ test for Residual Angle differences
```{r Mann-Whitney µ test for Residual Angle differences}

#Note: Equivalent to Wilcoxon rank sum test as data is unpaired

b5KD_Control <- subset(Residual_Angle_data, Residual_Angle_data$Condition == "b5KD" | Residual_Angle_data$Condition == "Control")
Utest.b5KD.vs.control <- wilcox.test(Pre.to.Post.Mitosis.Angle ~ Condition, data = b5KD_Control)
Utest.b5KD.vs.control.pval <- Utest.b5KD.vs.control$p.value

b5KD_Rescue <- subset(Residual_Angle_data, Residual_Angle_data$Condition == "b5KD" | Residual_Angle_data$Condition == "Rescue")
Utest.b5KD.vs.Rescue <- wilcox.test(Pre.to.Post.Mitosis.Angle ~ Condition, data = b5KD_Rescue)
Utest.b5KD.vs.Rescue.pval <- Utest.b5KD.vs.Rescue$p.value

Control_Rescue <- subset(Residual_Angle_data, Residual_Angle_data$Condition == "Control" | Residual_Angle_data$Condition == "Rescue")
Utest.control.vs.Rescue <- wilcox.test(Pre.to.Post.Mitosis.Angle ~ Condition, data = Control_Rescue)
Utest.control.vs.Rescue.pval <- Utest.control.vs.Rescue$p.value

Mann_Whitney_Scores <- data.frame(Condition_Comparison = c("b5KD_vs_Control", "b5KD_vs_Rescue", "Control_vs_Rescue"))
Mann_Whitney_Scores$pvalue <- rbind(Utest.b5KD.vs.control.pval, Utest.b5KD.vs.Rescue.pval, Utest.control.vs.Rescue.pval)

print(Mann_Whitney_Scores)

```

## Residual Angle Comparisons per Condition per Experiment
### Plot Residual Angle Boxplot by condition by experiment
```{r Plot Residual Angle Boxplot by condition by experiment}
Residual_Angle_dataFN <- Residual_Angle_data

Residual_Angle_dataFN$File.Name <- factor(Residual_Angle_dataFN$File.Name, c("_3_MMStack_control Hela 01.ome.tif", "_3_MMStack_control Hela 02.ome.tif", "_3_MMStack_b5 KD plus GFP 01.ome.tif", "_3_MMStack_b5 KD plus GFP 02.ome.tif", "_3_MMStack_b5 KD plus WTb5-GFP 01.ome.tif", "_3_MMStack_b5 KD plus WTb5-GFP 02.ome.tif"))


boxplot(Pre.to.Post.Mitosis.Angle ~ File.Name, data = Residual_Angle_dataFN, notch = T, varwidth = T, outline = F, ylab = "Residual Angle", col = c("#49494A", "#49494A", "#EC1C24", "#EC1C24","#BCBCBC", "#BCBCBC"), ylim = c(0,115), names = c("Ctrl_01", "Ctrl_02", "b5KD_01", "b5KD_02", "Resc_01", "Resc_02"))

# beeswarm(Pre.to.Post.Mitosis.Angle ~ File.Name, data = Residual_Angle_dataFN, method = 'swarm', add = T, col = "steelblue2", pch = 0, cex = 0.7)

```

### Plot Plot Residual Angle smoothed density plot per condition per experiment
```{r Plot Residual Angle smoothed density plot per condition per experiment}

sm.density.compare(Residual_Angle_dataFN$Pre.to.Post.Mitosis.Angle, Residual_Angle_dataFN$File.Name, col = c("#49494A", "#49494A", "#EC1C24", "#EC1C24","#BCBCBC","#BCBCBC"), h=5, lty = c(1,5,1,5,1,5), lwd = 2, xlim = c(0,90), xlab = "Residual Angle")
legend(x=23, y= 0.05, levels(Residual_Angle_dataFN$File.Name), fill=c("#49494A", "#49494A", "#EC1C24", "#EC1C24","#BCBCBC","#BCBCBC"), bty = 'n')

```
#### Count cell number per condition per experiment
```{r Count cell number per condition per experiment}
Residual_Angle_dataFN %>%
  group_by(File.Name) %>%
  summarise(
    n = n())
```



## Cell Division Defect Quantification
### Fig 8C Cell Division Defect Rate Plotting
```{r Cell Division Defect Rate Plotting}

CDD_boxplot_panel_file_name <- "Panel 8C Cell Division Defect Rate Boxplot"

Cell_Division_Defect_Quantification$Condition <- factor(Cell_Division_Defect_Quantification$Condition, c("Control", "b5KD", "Rescue"))

svglite(paste(panel_output_path, CDD_boxplot_panel_file_name, ".svg", sep = ""), width=4, height=6)

boxplot(normal.cell.division ~ Condition, data = Cell_Division_Defect_Quantification, outline = FALSE, boxlty = 0, whisklty = 0, staplelty = 0, ylab = "Normal Division Percentage", ylim = c(0,115), medlwd = 0.0)
beeswarm(normal.cell.division ~ Condition, data = Cell_Division_Defect_Quantification, method = 'center', add = T, col = c(control = "#49494A", b5KD = "#EC1C24", Rescue = "#BCBCBC"), ylim = c(0,115), pch = 19, cex = 1.3)

dev.off()

boxplot(normal.cell.division ~ Condition, data = Cell_Division_Defect_Quantification, outline = FALSE, boxlty = 0, whisklty = 0, staplelty = 0, ylab = "Normal Division Percentage", ylim = c(0,115), medlwd = 0.0)
beeswarm(normal.cell.division ~ Condition, data = Cell_Division_Defect_Quantification, method = 'center', add = T, col = c(control = "#49494A", b5KD = "#EC1C24", Rescue = "#BCBCBC"), ylim = c(0,115), pch = 19, cex = 1.3)

```

#### Cell Division Defect Summaries 
```{r Cell Division Defect Quantification}

Division_Defect_Summary <- Cell_Division_Defect_Quantification %>%
  group_by(Condition) %>%
  summarise(
    Normal_Division = mean(normal.cell.division),
    Abnormal_Division = mean(abnormal.cell.division)
  )
 
print(Division_Defect_Summary)   

```

#### T-testing for Cell Division Defect Rate differences
```{r T-testing for Cell Division Defect Rates differences}

b5KD_Control_CDD <- subset(Cell_Division_Defect_Quantification, Condition == "b5KD" | Condition == "Control")
T_test_b5KD_Control_CDD <- t.test(normal.cell.division ~ Condition, data = b5KD_Control_CDD)$p.value

b5KD_Rescue_CDD <- subset(Cell_Division_Defect_Quantification, Condition == "b5KD" | Condition == "Rescue")
T_test_b5KD_Rescue_CDD <- t.test(normal.cell.division ~ Condition, data = b5KD_Rescue_CDD)$p.value

Control_Rescue_CDD <- subset(Cell_Division_Defect_Quantification, Condition == "Control" | Condition == "Rescue")
T_test_Control_Rescue_CDD <- t.test(normal.cell.division ~ Condition, data = Control_Rescue_CDD)$p.value

Ttest_Scores_CDD <- data.frame(Condition_Comparison = c("b5KD_Control_CDD", "b5KD_Rescue_CDD", "Control_Rescue_CDD"))
Ttest_Scores_CDD$pvalue <- rbind(T_test_b5KD_Control_CDD, T_test_b5KD_Rescue_CDD, T_test_Control_Rescue_CDD)

print(Ttest_Scores_CDD)

```

# Supp Fig 5

## Import Data Supp Fig 5
```{r Import Data Supp Fig 5}
# Set your path to input data
data_input_path <- "/Users/johnlock/Dropbox/A-CMAC Manchester collaboration/NCB submission documents/Resubmission 2/Source_Data"
data_file <- "Supplementary Table 1 Statistics Source Data.xlsx"

# Import individual data files

Arp23_Inhibition_Data <- read_xlsx(paste(data_input_path, data_file, sep = "/"), sheet = "SuppFig5C", col_names = TRUE)

Arp23_Inhibition_Data$Drug <- recode(Arp23_Inhibition_Data$Drug, Arp_Inhib_CTRL_689 = "Inhib_CTRL_689")

```

## Analyse Arp2/3 inhibitor responses relative to control

### Boxplot changes in reticular adhesion b5 intensity2
```{r Boxplot changes in reticular adhesion b5 intensity2}
Arp23_b5_intensity_boxplots_filename <- "Panel SF5C Arp23_b5_intensity_boxplots"

cell.numbers.ARP23.screen <- count(Arp23_Inhibition_Data, Drug)
count(Arp23_Inhibition_Data, Drug)
mean.cell.numbers.ARP23.screen <- summarise(cell.numbers.ARP23.screen, avg = mean(n), SD = sd(n), sum(n))
summarise(cell.numbers.ARP23.screen, avg = mean(n), SD = sd(n), sum(n))


svglite(paste(panel_output_path, Arp23_b5_intensity_boxplots_filename, ".svg", sep = ""), width=7, height=5)

p1 <- ggplot(Arp23_Inhibition_Data, aes(x=Drug, y=Median.Mean..z.score, color = factor(Drug))) + 
  geom_boxplot(notch = TRUE, outlier.colour = "NA") + 
  scale_x_discrete(limits=c("Inhib_CTRL_689", "Arp_Inhib_666")) +
  theme(axis.text.x  = element_text(angle=45, hjust = 1)) +
  ylim(-3, 6) + ylab("Mean Reticular Adhesion Intensity") + theme(legend.position="none")


## plot T-CMAC intensity Z-score ggplot2 boxplot - per Drug 

p2 <- ggplot(Arp23_Inhibition_Data, aes(x=Drug, y=Median.Mean._T.CMACs.z.score, color = factor(Drug))) + 
  geom_boxplot(notch = TRUE, outlier.colour = "NA") + 
  scale_x_discrete(limits=c("Inhib_CTRL_689", "Arp_Inhib_666")) +
  theme(axis.text.x  = element_text(angle=45, hjust = 1)) +
  ylim(-3, 6) + ylab("Mean Focal Adhesion Intensity") + theme(legend.position="none")

## plot intensity ratio Z-score ggplot2 boxplot - per Drug 

p3 <- ggplot(Arp23_Inhibition_Data, aes(x=Drug, y=Ratio_A.CMAC.to.T.CMAC_mean_intensity.z.score, color = factor(Drug))) + 
  geom_boxplot(notch = TRUE, outlier.colour = "NA") + 
  scale_x_discrete(limits=c("Inhib_CTRL_689", "Arp_Inhib_666")) +
  theme(axis.text.x  = element_text(angle=45, hjust = 1)) +
  ylim(-3, 6) + ylab("Ratio Reticular:Focal Adhesion Intensity") + theme(legend.position="none")

grid.arrange(p1, p2, p3, ncol = 3)

dev.off()

p1 <- ggplot(Arp23_Inhibition_Data, aes(x=Drug, y=Median.Mean..z.score, color = factor(Drug))) + 
  geom_boxplot(notch = TRUE, outlier.colour = "NA") + 
  scale_x_discrete(limits=c("Inhib_CTRL_689", "Arp_Inhib_666")) +
  theme(axis.text.x  = element_text(angle=45, hjust = 1)) +
  ylim(-3, 6) + ylab("Mean Reticular Adhesion Intensity") + theme(legend.position="none")

## plot T-CMAC intensity Z-score ggplot2 boxplot - per Drug 

p2 <- ggplot(Arp23_Inhibition_Data, aes(x=Drug, y=Median.Mean._T.CMACs.z.score, color = factor(Drug))) + 
  geom_boxplot(notch = TRUE, outlier.colour = "NA") + 
  scale_x_discrete(limits=c("Inhib_CTRL_689", "Arp_Inhib_666")) +
  theme(axis.text.x  = element_text(angle=45, hjust = 1)) +
  ylim(-3, 6) + ylab("Mean Focal Adhesion Intensity") + theme(legend.position="none")

## plot intensity ratio Z-score ggplot2 boxplot - per Drug 

p3 <- ggplot(Arp23_Inhibition_Data, aes(x=Drug, y=Ratio_A.CMAC.to.T.CMAC_mean_intensity.z.score, color = factor(Drug))) + 
  geom_boxplot(notch = TRUE, outlier.colour = "NA") + 
  scale_x_discrete(limits=c("Inhib_CTRL_689", "Arp_Inhib_666")) +
  theme(axis.text.x  = element_text(angle=45, hjust = 1)) +
  ylim(-3, 6) + ylab("Ratio Reticular:Focal Adhesion Intensity") + theme(legend.position="none")

grid.arrange(p1, p2, p3, ncol = 3)

```

### Mann-Whitney test for significant Drug treatment effects on Reticular adhesion intensity2
```{r Mann-Whitney test for significant Drug treatment effects on Reticular adhesion intensity2}
p_values <- c()
for (i in 1:length(unique(Arp23_Inhibition_Data$Drug))){
  for (j in 1:length(unique(Arp23_Inhibition_Data$Drug))){
    Drug_1 = subset(Arp23_Inhibition_Data, Arp23_Inhibition_Data$Drug == unique(Arp23_Inhibition_Data$Drug)[i])
    Drug_1$Drug_Number = as.factor(i)
    Drug_2 = subset(Arp23_Inhibition_Data, Arp23_Inhibition_Data$Drug == unique(Arp23_Inhibition_Data$Drug)[j])
    Drug_2$Drug_Number = as.factor(j+100)
    Drug_Pair = rbind(Drug_1, Drug_2)
    Drug_Pair_ttest = wilcox.test(Median.Mean..z.score ~ Drug_Number, data = Drug_Pair)$p.value
    p_values = rbind(p_values, Drug_Pair_ttest)
  }
}

p_values <- as.vector(p_values)

p_value_matrix <- matrix(p_values,nrow = length(unique(Arp23_Inhibition_Data$Drug)),ncol = length(unique(Arp23_Inhibition_Data$Drug)))
columns <- as.vector(unique(Arp23_Inhibition_Data$Drug))
rows <- as.vector(unique(Arp23_Inhibition_Data$Drug))
p_value_dataframe <- as.data.frame(p_value_matrix, row.names = rows)
names(p_value_dataframe) <- columns

# print(p_value_dataframe)

# Introduce Holm (also called Holm-Bonferroni) p-value correction (used because it gives same stringency against false positives (type 1 errors) with lower probability of false negatives (type 2 errors)))

corrected_p_values <- p.adjust(p_values, method = "holm", n = length(p_values))

corrected_p_value_matrix6 <- matrix(corrected_p_values,nrow = length(unique(Arp23_Inhibition_Data$Drug)),ncol = length(unique(Arp23_Inhibition_Data$Drug)))
columns <- as.vector(unique(Arp23_Inhibition_Data$Drug))
rows <- as.vector(unique(Arp23_Inhibition_Data$Drug))
corrected_p_value_dataframe <- as.data.frame(corrected_p_value_matrix6, row.names = rows)
names(corrected_p_value_dataframe) <- columns

print(corrected_p_value_dataframe)
```

### Mann-Whitney test for significant Drug treatment effects on Focal adhesion intensity2
```{r Mann-Whitney test for significant Drug treatment effects on Focal adhesion intensity2}
p_values <- c()
for (i in 1:length(unique(Arp23_Inhibition_Data$Drug))){
  for (j in 1:length(unique(Arp23_Inhibition_Data$Drug))){
    Drug_1 = subset(Arp23_Inhibition_Data, Arp23_Inhibition_Data$Drug == unique(Arp23_Inhibition_Data$Drug)[i])
    Drug_1$Drug_Number = as.factor(i)
    Drug_2 = subset(Arp23_Inhibition_Data, Arp23_Inhibition_Data$Drug == unique(Arp23_Inhibition_Data$Drug)[j])
    Drug_2$Drug_Number = as.factor(j+100)
    Drug_Pair = rbind(Drug_1, Drug_2)
    Drug_Pair_ttest = wilcox.test(Median.Mean._T.CMACs.z.score ~ Drug_Number, data = Drug_Pair)$p.value
    p_values = rbind(p_values, Drug_Pair_ttest)
  }
}

p_values <- as.vector(p_values)

p_value_matrix <- matrix(p_values,nrow = length(unique(Arp23_Inhibition_Data$Drug)),ncol = length(unique(Arp23_Inhibition_Data$Drug)))
columns <- as.vector(unique(Arp23_Inhibition_Data$Drug))
rows <- as.vector(unique(Arp23_Inhibition_Data$Drug))
p_value_dataframe <- as.data.frame(p_value_matrix, row.names = rows)
names(p_value_dataframe) <- columns

# print(p_value_dataframe)

# Introduce Holm (also called Holm-Bonferroni) p-value correction (used because it gives same stringency against false positives (type 1 errors) with lower probability of false negatives (type 2 errors)))

corrected_p_values <- p.adjust(p_values, method = "holm", n = length(p_values))

corrected_p_value_matrix7 <- matrix(corrected_p_values,nrow = length(unique(Arp23_Inhibition_Data$Drug)),ncol = length(unique(Arp23_Inhibition_Data$Drug)))
columns <- as.vector(unique(Arp23_Inhibition_Data$Drug))
rows <- as.vector(unique(Arp23_Inhibition_Data$Drug))
corrected_p_value_dataframe <- as.data.frame(corrected_p_value_matrix7, row.names = rows)
names(corrected_p_value_dataframe) <- columns

print(corrected_p_value_dataframe)
```

### Mann-Whitney test for significant Drug treatment effects on intensity ratio Z-score2
```{r Mann-Whitney test for significant Drug treatment effects on intensity ratio Z-score2}
p_values <- c()
for (i in 1:length(unique(Arp23_Inhibition_Data$Drug))){
  for (j in 1:length(unique(Arp23_Inhibition_Data$Drug))){
    Drug_1 = subset(Arp23_Inhibition_Data, Arp23_Inhibition_Data$Drug == unique(Arp23_Inhibition_Data$Drug)[i])
    Drug_1$Drug_Number = as.factor(i)
    Drug_2 = subset(Arp23_Inhibition_Data, Arp23_Inhibition_Data$Drug == unique(Arp23_Inhibition_Data$Drug)[j])
    Drug_2$Drug_Number = as.factor(j+100)
    Drug_Pair = rbind(Drug_1, Drug_2)
    Drug_Pair_ttest = wilcox.test(Ratio_A.CMAC.to.T.CMAC_mean_intensity.z.score ~ Drug_Number, data = Drug_Pair)$p.value 
    p_values = rbind(p_values, Drug_Pair_ttest)
  }
}

p_values <- as.vector(p_values)

p_value_matrix <- matrix(p_values,nrow = length(unique(Arp23_Inhibition_Data$Drug)),ncol = length(unique(Arp23_Inhibition_Data$Drug)))
columns <- as.vector(unique(Arp23_Inhibition_Data$Drug))
rows <- as.vector(unique(Arp23_Inhibition_Data$Drug))
p_value_dataframe <- as.data.frame(p_value_matrix, row.names = rows)
names(p_value_dataframe) <- columns

# print(p_value_dataframe)

# Introduce Holm (also called Holm-Bonferroni) p-value correction (used because it gives same stringency against false positives (type 1 errors) with lower probability of false negatives (type 2 errors)))

corrected_p_values <- p.adjust(p_values, method = "holm", n = length(p_values))

corrected_p_value_matrix8 <- matrix(corrected_p_values,nrow = length(unique(Arp23_Inhibition_Data$Drug)),ncol = length(unique(Arp23_Inhibition_Data$Drug)))
columns <- as.vector(unique(Arp23_Inhibition_Data$Drug))
rows <- as.vector(unique(Arp23_Inhibition_Data$Drug))
corrected_p_value_dataframe <- as.data.frame(corrected_p_value_matrix8, row.names = rows)
names(corrected_p_value_dataframe) <- columns

print(corrected_p_value_dataframe)
```

# Supp Fig 6

## Import Data Supp Fig 6
```{r Import Data Supp Fig 6}
# Set your path to input data
data_input_path <- "/Users/johnlock/Dropbox/A-CMAC Manchester collaboration/NCB submission documents/Resubmission 2/Source_Data"
data_file <- "Supplementary Table 1 Statistics Source Data.xlsx"

# Import individual data files

Secondary.siRNA.Screen.Data <-  read_xlsx(paste(data_input_path, data_file, sep = "/"), sheet = "SuppFig6A", col_names = TRUE)

```

## Analyse Secondary siRNA Screen responses

### Boxplot changes in reticular to focal adhesion b5 intensity ratio
```{r Boxplot changes in reticular to focal adhesion b5 intensity ratio}
Secondary_siRNA_Screen_boxplots_filename <- "Panel SF6A Secondary_siRNA_Screen_boxplot_summary"

Secondary.siRNA.Screen.Data$siRNA_Target <- recode(Secondary.siRNA.Screen.Data$siRNA_Target, PIK3C2a_1 = "PIK3C2A_1")
Secondary.siRNA.Screen.Data$siRNA_Target <- recode(Secondary.siRNA.Screen.Data$siRNA_Target, PIK3C2a_2 = "PIK3C2A_2")
Secondary.siRNA.Screen.Data$siRNA_Target <- recode(Secondary.siRNA.Screen.Data$siRNA_Target, PIK3C2a_3 = "PIK3C2A_3")
Secondary.siRNA.Screen.Data$siRNA_Target <- recode(Secondary.siRNA.Screen.Data$siRNA_Target, PIK3C2a_4 = "PIK3C2A_4")

cell.numbers.secondary.screen <- count(Secondary.siRNA.Screen.Data, siRNA_Target)
count(Secondary.siRNA.Screen.Data, siRNA_Target)
mean.cell.numbers.secondary.screen <- summarise(cell.numbers.secondary.screen, avg = mean(n), SD = sd(n), sum(n))
summarise(cell.numbers.secondary.screen, avg = mean(n), SD = sd(n), sum(n))

svglite(paste(panel_output_path, Secondary_siRNA_Screen_boxplots_filename, ".svg", sep = ""), width=7, height=5)

ggplot(Secondary.siRNA.Screen.Data, aes(x=siRNA_Target, y=Ratio_A.CMAC.to.T.CMAC_mean_intensity.z.score, color = factor(siRNA_Treatment_Type))) +
  geom_boxplot(notch = TRUE, outlier.colour = "NA")  +
  theme(axis.text.x  = element_text(angle=90)) +
  ylim(-3, 6) + 
  scale_x_discrete(limits=c("Untreated","MOCK","SST_3Ctrl_Pool", "ON-TARGETplus Non-targeting Control",
                          "siGENOME Non-targeting Control", "EGFP", "ITGAV", "ITGB5", "OCRL_1", "OCRL_2", "OCRL_3", "OCRL_4", "PI4K2A_1", "PI4K2A_2", "PI4K2A_3", "PI4K2A_4", "PI4KA_1", "PI4KA_2", "PI4KA_3", "PI4KA_4", "PIP5K1B_1", "PIP5K1B_2", "PIP5K1B_3", "PIP5K1B_4", "PIP5K1C_1", "PIP5K1C_2", "PIP5K1C_3", "PIP5K1C_4", "PTEN_1", "PTEN_2", "PTEN_3", "PTEN_4", "PIK3C2A_1", "PIK3C2A_2", "PIK3C2A_3", "PIK3C2A_4"))

dev.off()

ggplot(Secondary.siRNA.Screen.Data, aes(x=siRNA_Target, y=Ratio_A.CMAC.to.T.CMAC_mean_intensity.z.score, color = factor(siRNA_Treatment_Type))) +
  geom_boxplot(notch = TRUE, outlier.colour = "NA")  +
  theme(axis.text.x  = element_text(angle=90)) +
  ylim(-3, 6) + 
  scale_x_discrete(limits=c("Untreated","MOCK","SST_3Ctrl_Pool", "ON-TARGETplus Non-targeting Control",
                          "siGENOME Non-targeting Control", "EGFP", "ITGAV", "ITGB5", "OCRL_1", "OCRL_2", "OCRL_3", "OCRL_4", "PI4K2A_1", "PI4K2A_2", "PI4K2A_3", "PI4K2A_4", "PI4KA_1", "PI4KA_2", "PI4KA_3", "PI4KA_4", "PIP5K1B_1", "PIP5K1B_2", "PIP5K1B_3", "PIP5K1B_4", "PIP5K1C_1", "PIP5K1C_2", "PIP5K1C_3", "PIP5K1C_4", "PTEN_1", "PTEN_2", "PTEN_3", "PTEN_4", "PIK3C2A_1", "PIK3C2A_2", "PIK3C2A_3", "PIK3C2A_4"))

```

### Mann-Whitney test for significant siRNA treatment effects2
```{r Mann-Whitney test for significant siRNA treatment effects2}
siRNA.conditions.for.U.test <- subset(Secondary.siRNA.Screen.Data, siRNA_Target != "Untreated" & siRNA_Target != "MOCK" & siRNA_Target != "SST_3Ctrl_Pool" & siRNA_Target != "siGENOME Non-targeting Control") 

p_values <- c()
for (i in 1:length(unique(siRNA.conditions.for.U.test$siRNA_Target))){
  for (j in 1:length(unique(siRNA.conditions.for.U.test$siRNA_Target))){
    siRNA_Target_1 = subset(siRNA.conditions.for.U.test, siRNA.conditions.for.U.test$siRNA_Target == unique(siRNA.conditions.for.U.test$siRNA_Target)[i])
    siRNA_Target_1$siRNA_Target_Number = as.factor(i)
    siRNA_Target_2 = subset(siRNA.conditions.for.U.test, siRNA.conditions.for.U.test$siRNA_Target == unique(siRNA.conditions.for.U.test$siRNA_Target)[j])
    siRNA_Target_2$siRNA_Target_Number = as.factor(j+100)
    siRNA_Target_Pair = rbind(siRNA_Target_1, siRNA_Target_2)
    siRNA_Target_Pair_ttest = wilcox.test(Ratio_A.CMAC.to.T.CMAC_mean_intensity.z.score ~ siRNA_Target_Number, data = siRNA_Target_Pair)$p.value
    p_values = rbind(p_values, siRNA_Target_Pair_ttest)
  }
}

p_values <- as.vector(p_values)

p_value_matrix <- matrix(p_values,nrow = length(unique(siRNA.conditions.for.U.test$siRNA_Target)),ncol = length(unique(siRNA.conditions.for.U.test$siRNA_Target)))
columns <- as.vector(unique(siRNA.conditions.for.U.test$siRNA_Target))
rows <- as.vector(unique(siRNA.conditions.for.U.test$siRNA_Target))
p_value_dataframe <- as.data.frame(p_value_matrix, row.names = rows)
names(p_value_dataframe) <- columns

# print(p_value_dataframe)

# Introduce Holm (also called Holm-Bonferroni) p-value correction (used because it gives same stringency against false positives (type 1 errors) with lower probability of false negatives (type 2 errors)))

corrected_p_values <- p.adjust(p_values, method = "holm", n = length(p_values))

corrected_p_value_matrix9 <- matrix(corrected_p_values,nrow = length(unique(siRNA.conditions.for.U.test$siRNA_Target)),ncol = length(unique(siRNA.conditions.for.U.test$siRNA_Target)))
columns <- as.vector(unique(siRNA.conditions.for.U.test$siRNA_Target))
rows <- as.vector(unique(siRNA.conditions.for.U.test$siRNA_Target))
corrected_p_value_dataframe <- as.data.frame(corrected_p_value_matrix9, row.names = rows)
names(corrected_p_value_dataframe) <- columns

print(corrected_p_value_dataframe)

```

### Plot heatmap of corrected p_values2
```{r Plot heatmap of corrected p_values2, fig.height=8, fig.width=8}
heatmap.2(log(corrected_p_value_matrix9), dendrogram = 'none', Rowv = FALSE, Colv = FALSE, labRow = rows, labCol = columns, srtCol = 45, cexRow = 1, cexCol = 1, margins = c(7, 9), symm = TRUE, revC = FALSE, breaks = c(log(1e-300), log(1e-50), log(1e-10), log(1e-6), log(0.001), log(0.01), log(0.05), log(1)), rowsep = 1:nrow(corrected_p_value_matrix9), colsep = 1:ncol(corrected_p_value_matrix9), sepcolor = 'darkgrey', sepwidth = c(0.02,0.02), trace = 'none', col = c("darkred", "red", "orange", "green", "blue", "purple", "lightgrey"), denscol = NULL, keysize = 1.5, key.title = NA)
```